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#' Summarize change from baseline values or absolute baseline values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [summarize_change()] creates a layout element to summarize the change from baseline or absolute
#' baseline values. The primary analysis variable `vars` indicates the numerical change from baseline results.
#'
#' Required secondary analysis variables `value` and `baseline_flag` can be supplied to the function via
#' the `variables` argument. The `value` element should be the name of the analysis value variable, and the
#' `baseline_flag` element should be the name of the flag variable that indicates whether or not records contain
#' baseline values. Depending on the baseline flag given, either the absolute baseline values (at baseline)
#' or the change from baseline values (post-baseline) are then summarized.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("analyze_vars_numeric"), type = "sh")``
#'
#' @name summarize_change
#' @order 1
NULL

#' @describeIn summarize_change Statistics function that summarizes baseline or post-baseline visits.
#'
#' @return
#' * `s_change_from_baseline()` returns the same values returned by [s_summary.numeric()].
#'
#' @note The data in `df` must be either all be from baseline or post-baseline visits. Otherwise
#'   an error will be thrown.
#'
#' @keywords internal
s_change_from_baseline <- function(df, ...) {
  args_list <- list(...)
  .var <- args_list[[".var"]]
  variables <- args_list[["variables"]]

  checkmate::assert_numeric(df[[variables$value]])
  checkmate::assert_numeric(df[[.var]])
  checkmate::assert_logical(df[[variables$baseline_flag]])
  checkmate::assert_vector(unique(df[[variables$baseline_flag]]), max.len = 1)
  assert_df_with_variables(df, c(variables, list(chg = .var)))

  combined <- ifelse(
    df[[variables$baseline_flag]],
    df[[variables$value]],
    df[[.var]]
  )
  if (is.logical(combined) && identical(length(combined), 0L)) {
    combined <- numeric(0)
  }
  s_summary(combined, ...)
}

#' @describeIn summarize_change Formatted analysis function which is used as `afun` in `summarize_change()`.
#'
#' @return
#' * `a_change_from_baseline()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_change_from_baseline <- function(df,
                                   ...,
                                   .stats = NULL,
                                   .formats = NULL,
                                   .labels = NULL,
                                   .indent_mods = NULL) {
  dots_extra_args <- list(...)

  # Check if there are user-defined functions
  default_and_custom_stats_list <- .split_std_from_custom_stats(.stats)
  .stats <- default_and_custom_stats_list$default_stats
  custom_stat_functions <- default_and_custom_stats_list$custom_stats

  # Adding automatically extra parameters to the statistic function (see ?rtables::additional_fun_params)
  extra_afun_params <- retrieve_extra_afun_params(
    names(dots_extra_args$.additional_fun_parameters)
  )
  dots_extra_args$.additional_fun_parameters <- NULL # After extraction we do not need them anymore

  # Main stats calculations
  x_stats <- .apply_stat_functions(
    default_stat_fnc = s_change_from_baseline,
    custom_stat_fnc_list = custom_stat_functions,
    args_list = c(
      df = list(df),
      extra_afun_params,
      dots_extra_args
    )
  )

  # Fill in with formatting defaults if needed
  .stats <- c(
    get_stats("analyze_vars_numeric", stats_in = .stats),
    names(custom_stat_functions) # Additional stats from custom functions
  )
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  # Auto format handling
  .formats <- apply_auto_formatting(
    .formats,
    x_stats,
    extra_afun_params$.df_row,
    extra_afun_params$.var
  )

  in_rows(
    .list = x_stats[.stats],
    .formats = .formats,
    .names = names(.labels),
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls()
  )
}

#' @describeIn summarize_change Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `summarize_change()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_change_from_baseline()` to the table layout.
#'
#' @note To be used after a split on visits in the layout, such that each data subset only contains
#'   either baseline or post-baseline data.
#'
#' @examples
#' library(dplyr)
#'
#' # Fabricate dataset
#' dta_test <- data.frame(
#'   USUBJID = rep(1:6, each = 3),
#'   AVISIT = rep(paste0("V", 1:3), 6),
#'   ARM = rep(LETTERS[1:3], rep(6, 3)),
#'   AVAL = c(9:1, rep(NA, 9))
#' ) %>%
#'   mutate(ABLFLL = AVISIT == "V1") %>%
#'   group_by(USUBJID) %>%
#'   mutate(
#'     BLVAL = AVAL[ABLFLL],
#'     CHG = AVAL - BLVAL
#'   ) %>%
#'   ungroup()
#'
#' results <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL")) %>%
#'   build_table(dta_test)
#'
#' results
#'
#' @export
#' @order 2
summarize_change <- function(lyt,
                             vars,
                             variables,
                             var_labels = vars,
                             na_str = default_na_str(),
                             na_rm = TRUE,
                             nested = TRUE,
                             show_labels = "default",
                             table_names = vars,
                             section_div = NA_character_,
                             ...,
                             .stats = c("n", "mean_sd", "median", "range"),
                             .formats = c(
                               n = "xx",
                               mean_sd = "xx.xx (xx.xx)",
                               mean_se = "xx.xx (xx.xx)",
                               median = "xx.xx",
                               range = "xx.xx - xx.xx",
                               mean_ci = "(xx.xx, xx.xx)",
                               median_ci = "(xx.xx, xx.xx)",
                               mean_pval = "xx.xx"
                             ),
                             .labels = c(
                               mean_sd = "Mean (SD)",
                               mean_se = "Mean (SE)",
                               median = "Median",
                               range = "Min - Max"
                             ),
                             .indent_mods = NULL) {
  # Extra args must contain .stats, .formats, .labels, .indent_mods - sent to the analysis level
  extra_args <- list(".stats" = .stats)
  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  # Adding additional arguments to the analysis function (depends on the specific call)
  extra_args <- c(extra_args, "na_rm" = na_rm, "variables" = list(variables), ...)

  # Adding all additional information from layout to analysis functions (see ?rtables::additional_fun_params)
  extra_args[[".additional_fun_parameters"]] <- get_additional_afun_params(add_alt_df = FALSE)
  formals(a_change_from_baseline) <- c(
    formals(a_change_from_baseline),
    extra_args[[".additional_fun_parameters"]]
  )

  # Main analysis call - Nothing with .* -> these should be dedicated to the analysis function
  analyze(
    lyt = lyt,
    vars = vars,
    var_labels = var_labels,
    afun = a_change_from_baseline,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    inclNAs = na_rm,
    show_labels = show_labels,
    table_names = table_names,
    section_div = section_div
  )
}

#' Odds ratio estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [estimate_odds_ratio()] creates a layout element to compare bivariate responses between
#' two groups by estimating an odds ratio and its confidence interval.
#'
#' The primary analysis variable specified by `vars` is the group variable. Additional variables can be included in the
#' analysis via the `variables` argument, which accepts `arm`, an arm variable, and `strata`, a stratification variable.
#' If more than two arm levels are present, they can be combined into two groups using the `groups_list` argument.
#'
#' @inheritParams split_cols_by_groups
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("estimate_odds_ratio"), type = "sh")``
#' @param method (`string`)\cr whether to use the correct (`"exact"`) calculation in the conditional likelihood or one
#'   of the approximations. See [survival::clogit()] for details.
#'
#' @note
#' * This function uses logistic regression for unstratified analyses, and conditional logistic regression for
#'   stratified analyses. The Wald confidence interval is calculated with the specified confidence level.
#' * For stratified analyses, there is currently no implementation for conditional likelihood confidence intervals,
#'   therefore the likelihood confidence interval is not available as an option.
#' * When `vars` contains only responders or non-responders no odds ratio estimation is possible so the returned
#'   values will be `NA`.
#'
#' @seealso Relevant helper function [h_odds_ratio()].
#'
#' @name odds_ratio
#' @order 1
NULL

#' @describeIn odds_ratio Statistics function which estimates the odds ratio
#'   between a treatment and a control. A `variables` list with `arm` and `strata`
#'   variable names must be passed if a stratified analysis is required.
#'
#' @return
#' * `s_odds_ratio()` returns a named list with the statistics `or_ci`
#'   (containing `est`, `lcl`, and `ucl`) and `n_tot`.
#'
#' @examples
#' # Unstratified analysis.
#' s_odds_ratio(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   .df_row = dta
#' )
#'
#' # Stratified analysis.
#' s_odds_ratio(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   .df_row = dta,
#'   variables = list(arm = "grp", strata = "strata")
#' )
#'
#' @export
s_odds_ratio <- function(df,
                         .var,
                         .ref_group,
                         .in_ref_col,
                         .df_row,
                         variables = list(arm = NULL, strata = NULL),
                         conf_level = 0.95,
                         groups_list = NULL,
                         method = "exact") {
  y <- list(or_ci = "", n_tot = "")

  if (!.in_ref_col) {
    assert_proportion_value(conf_level)
    assert_df_with_variables(df, list(rsp = .var))
    assert_df_with_variables(.ref_group, list(rsp = .var))

    if (is.null(variables$strata)) {
      data <- data.frame(
        rsp = c(.ref_group[[.var]], df[[.var]]),
        grp = factor(
          rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
          levels = c("ref", "Not-ref")
        )
      )
      y <- or_glm(data, conf_level = conf_level)
    } else {
      assert_df_with_variables(.df_row, c(list(rsp = .var), variables))
      checkmate::assert_subset(method, c("exact", "approximate", "efron", "breslow"), empty.ok = FALSE)

      # The group variable prepared for clogit must be synchronised with combination groups definition.
      if (is.null(groups_list)) {
        ref_grp <- as.character(unique(.ref_group[[variables$arm]]))
        trt_grp <- as.character(unique(df[[variables$arm]]))
        grp <- stats::relevel(factor(.df_row[[variables$arm]]), ref = ref_grp)
      } else {
        # If more than one level in reference col.
        reference <- as.character(unique(.ref_group[[variables$arm]]))
        grp_ref_flag <- vapply(
          X = groups_list,
          FUN.VALUE = TRUE,
          FUN = function(x) all(reference %in% x)
        )
        ref_grp <- names(groups_list)[grp_ref_flag]

        # If more than one level in treatment col.
        treatment <- as.character(unique(df[[variables$arm]]))
        grp_trt_flag <- vapply(
          X = groups_list,
          FUN.VALUE = TRUE,
          FUN = function(x) all(treatment %in% x)
        )
        trt_grp <- names(groups_list)[grp_trt_flag]

        grp <- combine_levels(.df_row[[variables$arm]], levels = reference, new_level = ref_grp)
        grp <- combine_levels(grp, levels = treatment, new_level = trt_grp)
      }

      # The reference level in `grp` must be the same as in the `rtables` column split.
      data <- data.frame(
        rsp = .df_row[[.var]],
        grp = grp,
        strata = interaction(.df_row[variables$strata])
      )
      y_all <- or_clogit(data, conf_level = conf_level, method = method)
      checkmate::assert_string(trt_grp)
      checkmate::assert_subset(trt_grp, names(y_all$or_ci))
      y$or_ci <- y_all$or_ci[[trt_grp]]
      y$n_tot <- y_all$n_tot
    }
  }

  if ("est" %in% names(y$or_ci) && is.na(y$or_ci[["est"]]) && method != "approximate") {
    warning(
      "Unable to compute the odds ratio estimate. Please try re-running the function with ",
      'parameter `method` set to "approximate".'
    )
  }

  y$or_ci <- formatters::with_label(
    x = y$or_ci,
    label = paste0("Odds Ratio (", 100 * conf_level, "% CI)")
  )

  y$n_tot <- formatters::with_label(
    x = y$n_tot,
    label = "Total n"
  )

  y
}

#' @describeIn odds_ratio Formatted analysis function which is used as `afun` in `estimate_odds_ratio()`.
#'
#' @return
#' * `a_odds_ratio()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_odds_ratio(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   .df_row = dta
#' )
#'
#' @export
a_odds_ratio <- make_afun(
  s_odds_ratio,
  .formats = c(or_ci = "xx.xx (xx.xx - xx.xx)"),
  .indent_mods = c(or_ci = 1L)
)

#' @describeIn odds_ratio Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_odds_ratio()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_odds_ratio()` to the table layout.
#'
#' @examples
#' set.seed(12)
#' dta <- data.frame(
#'   rsp = sample(c(TRUE, FALSE), 100, TRUE),
#'   grp = factor(rep(c("A", "B"), each = 50), levels = c("A", "B")),
#'   strata = factor(sample(c("C", "D"), 100, TRUE))
#' )
#'
#' l <- basic_table() %>%
#'   split_cols_by(var = "grp", ref_group = "B") %>%
#'   estimate_odds_ratio(vars = "rsp")
#'
#' build_table(l, df = dta)
#'
#' @export
#' @order 2
estimate_odds_ratio <- function(lyt,
                                vars,
                                variables = list(arm = NULL, strata = NULL),
                                conf_level = 0.95,
                                groups_list = NULL,
                                na_str = default_na_str(),
                                nested = TRUE,
                                method = "exact",
                                show_labels = "hidden",
                                table_names = vars,
                                var_labels = vars,
                                .stats = "or_ci",
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL) {
  extra_args <- list(variables = variables, conf_level = conf_level, groups_list = groups_list, method = method)

  afun <- make_afun(
    a_odds_ratio,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    afun = afun,
    var_labels = var_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Helper functions for odds ratio estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions to calculate odds ratios in [estimate_odds_ratio()].
#'
#' @inheritParams odds_ratio
#' @inheritParams argument_convention
#' @param data (`data.frame`)\cr data frame containing at least the variables `rsp` and `grp`, and optionally
#'   `strata` for [or_clogit()].
#'
#' @return A named `list` of elements `or_ci` and `n_tot`.
#'
#' @seealso [odds_ratio]
#'
#' @name h_odds_ratio
NULL

#' @describeIn h_odds_ratio Estimates the odds ratio based on [stats::glm()]. Note that there must be
#'   exactly 2 groups in `data` as specified by the `grp` variable.
#'
#' @examples
#' # Data with 2 groups.
#' data <- data.frame(
#'   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),
#'   grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],
#'   strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],
#'   stringsAsFactors = TRUE
#' )
#'
#' # Odds ratio based on glm.
#' or_glm(data, conf_level = 0.95)
#'
#' @export
or_glm <- function(data, conf_level) {
  checkmate::assert_logical(data$rsp)
  assert_proportion_value(conf_level)
  assert_df_with_variables(data, list(rsp = "rsp", grp = "grp"))
  checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))

  data$grp <- as_factor_keep_attributes(data$grp)
  assert_df_with_factors(data, list(val = "grp"), min.levels = 2, max.levels = 2)
  formula <- stats::as.formula("rsp ~ grp")
  model_fit <- stats::glm(
    formula = formula, data = data,
    family = stats::binomial(link = "logit")
  )

  # Note that here we need to discard the intercept.
  or <- exp(stats::coef(model_fit)[-1])
  or_ci <- exp(
    stats::confint.default(model_fit, level = conf_level)[-1, , drop = FALSE]
  )

  values <- stats::setNames(c(or, or_ci), c("est", "lcl", "ucl"))
  n_tot <- stats::setNames(nrow(model_fit$model), "n_tot")

  list(or_ci = values, n_tot = n_tot)
}

#' @describeIn h_odds_ratio Estimates the odds ratio based on [survival::clogit()]. This is done for
#'   the whole data set including all groups, since the results are not the same as when doing
#'   pairwise comparisons between the groups.
#'
#' @examples
#' # Data with 3 groups.
#' data <- data.frame(
#'   rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),
#'   grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],
#'   strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],
#'   stringsAsFactors = TRUE
#' )
#'
#' # Odds ratio based on stratified estimation by conditional logistic regression.
#' or_clogit(data, conf_level = 0.95)
#'
#' @export
or_clogit <- function(data, conf_level, method = "exact") {
  checkmate::assert_logical(data$rsp)
  assert_proportion_value(conf_level)
  assert_df_with_variables(data, list(rsp = "rsp", grp = "grp", strata = "strata"))
  checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))
  checkmate::assert_multi_class(data$strata, classes = c("factor", "character"))
  checkmate::assert_subset(method, c("exact", "approximate", "efron", "breslow"), empty.ok = FALSE)

  data$grp <- as_factor_keep_attributes(data$grp)
  data$strata <- as_factor_keep_attributes(data$strata)

  # Deviation from convention: `survival::strata` must be simply `strata`.
  formula <- stats::as.formula("rsp ~ grp + strata(strata)")
  model_fit <- clogit_with_tryCatch(formula = formula, data = data, method = method)

  # Create a list with one set of OR estimates and CI per coefficient, i.e.
  # comparison of one group vs. the reference group.
  coef_est <- stats::coef(model_fit)
  ci_est <- stats::confint(model_fit, level = conf_level)
  or_ci <- list()
  for (coef_name in names(coef_est)) {
    grp_name <- gsub("^grp", "", x = coef_name)
    or_ci[[grp_name]] <- stats::setNames(
      object = exp(c(coef_est[coef_name], ci_est[coef_name, , drop = TRUE])),
      nm = c("est", "lcl", "ucl")
    )
  }
  list(or_ci = or_ci, n_tot = c(n_tot = model_fit$n))
}

#' Confidence intervals for a difference of binomials
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Several confidence intervals for the difference between proportions.
#'
#' @name desctools_binom
NULL

#' Recycle list of parameters
#'
#' This function recycles all supplied elements to the maximal dimension.
#'
#' @param ... (`any`)\cr elements to recycle.
#'
#' @return A `list`.
#'
#' @keywords internal
#' @noRd
h_recycle <- function(...) {
  lst <- list(...)
  maxdim <- max(lengths(lst))
  res <- lapply(lst, rep, length.out = maxdim)
  attr(res, "maxdim") <- maxdim
  return(res)
}

#' @describeIn desctools_binom Several confidence intervals for the difference between proportions.
#'
#' @return A `matrix` of 3 values:
#'   * `est`: estimate of proportion difference.
#'   * `lwr.ci`: estimate of lower end of the confidence interval.
#'   * `upr.ci`: estimate of upper end of the confidence interval.
#'
#' @keywords internal
desctools_binom <- function(x1,
                            n1,
                            x2,
                            n2,
                            conf.level = 0.95, # nolint
                            sides = c("two.sided", "left", "right"),
                            method = c(
                              "ac", "wald", "waldcc", "score", "scorecc", "mn", "mee", "blj", "ha", "hal", "jp"
                            )) {
  if (missing(sides)) {
    sides <- match.arg(sides)
  }
  if (missing(method)) {
    method <- match.arg(method)
  }
  iBinomDiffCI <- function(x1, n1, x2, n2, conf.level, sides, method) { # nolint
    if (sides != "two.sided") {
      conf.level <- 1 - 2 * (1 - conf.level) # nolint
    }
    alpha <- 1 - conf.level
    kappa <- stats::qnorm(1 - alpha / 2)
    p1_hat <- x1 / n1
    p2_hat <- x2 / n2
    est <- p1_hat - p2_hat
    switch(method,
      wald = {
        vd <- p1_hat * (1 - p1_hat) / n1 + p2_hat * (1 - p2_hat) / n2
        term2 <- kappa * sqrt(vd)
        ci_lwr <- max(-1, est - term2)
        ci_upr <- min(1, est + term2)
      },
      waldcc = {
        vd <- p1_hat * (1 - p1_hat) / n1 + p2_hat * (1 - p2_hat) / n2
        term2 <- kappa * sqrt(vd)
        term2 <- term2 + 0.5 * (1 / n1 + 1 / n2)
        ci_lwr <- max(-1, est - term2)
        ci_upr <- min(1, est + term2)
      },
      ac = {
        n1 <- n1 + 2
        n2 <- n2 + 2
        x1 <- x1 + 1
        x2 <- x2 + 1
        p1_hat <- x1 / n1
        p2_hat <- x2 / n2
        est1 <- p1_hat - p2_hat
        vd <- p1_hat * (1 - p1_hat) / n1 + p2_hat * (1 - p2_hat) / n2
        term2 <- kappa * sqrt(vd)
        ci_lwr <- max(-1, est1 - term2)
        ci_upr <- min(1, est1 + term2)
      },
      exact = {
        ci_lwr <- NA
        ci_upr <- NA
      },
      score = {
        w1 <- desctools_binomci(
          x = x1, n = n1, conf.level = conf.level,
          method = "wilson"
        )
        w2 <- desctools_binomci(
          x = x2, n = n2, conf.level = conf.level,
          method = "wilson"
        )
        l1 <- w1[2]
        u1 <- w1[3]
        l2 <- w2[2]
        u2 <- w2[3]
        ci_lwr <- est - kappa * sqrt(l1 * (1 - l1) / n1 + u2 * (1 - u2) / n2)
        ci_upr <- est + kappa * sqrt(u1 * (1 - u1) / n1 + l2 * (1 - l2) / n2)
      },
      scorecc = {
        w1 <- desctools_binomci(
          x = x1, n = n1, conf.level = conf.level,
          method = "wilsoncc"
        )
        w2 <- desctools_binomci(
          x = x2, n = n2, conf.level = conf.level,
          method = "wilsoncc"
        )
        l1 <- w1[2]
        u1 <- w1[3]
        l2 <- w2[2]
        u2 <- w2[3]
        ci_lwr <- max(-1, est - sqrt((p1_hat - l1)^2 + (u2 - p2_hat)^2))
        ci_upr <- min(1, est + sqrt((u1 - p1_hat)^2 + (p2_hat - l2)^2))
      },
      mee = {
        .score <- function(p1, n1, p2, n2, dif) {
          if (dif > 1) dif <- 1
          if (dif < -1) dif <- -1
          diff <- p1 - p2 - dif
          if (abs(diff) == 0) {
            res <- 0
          } else {
            t <- n2 / n1
            a <- 1 + t
            b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
            c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
            d <- -p1 * dif * (1 + dif)
            v <- (b / a / 3)^3 - b * c / (6 * a * a) + d / a / 2
            if (abs(v) < .Machine$double.eps) v <- 0
            s <- sqrt((b / a / 3)^2 - c / a / 3)
            u <- ifelse(v > 0, 1, -1) * s
            w <- (3.141592654 + acos(v / u^3)) / 3
            p1d <- 2 * u * cos(w) - b / a / 3
            p2d <- p1d - dif
            n <- n1 + n2
            res <- (p1d * (1 - p1d) / n1 + p2d * (1 - p2d) / n2)
          }
          return(sqrt(res))
        }
        pval <- function(delta) {
          z <- (est - delta) / .score(p1_hat, n1, p2_hat, n2, delta)
          2 * min(stats::pnorm(z), 1 - stats::pnorm(z))
        }
        ci_lwr <- max(-1, stats::uniroot(function(delta) {
          pval(delta) - alpha
        }, interval = c(-1 + 1e-06, est - 1e-06))$root)
        ci_upr <- min(1, stats::uniroot(function(delta) {
          pval(delta) - alpha
        }, interval = c(est + 1e-06, 1 - 1e-06))$root)
      },
      blj = {
        p1_dash <- (x1 + 0.5) / (n1 + 1)
        p2_dash <- (x2 + 0.5) / (n2 + 1)
        vd <- p1_dash * (1 - p1_dash) / n1 + p2_dash * (1 - p2_dash) / n2
        term2 <- kappa * sqrt(vd)
        est_dash <- p1_dash - p2_dash
        ci_lwr <- max(-1, est_dash - term2)
        ci_upr <- min(1, est_dash + term2)
      },
      ha = {
        term2 <- 1 /
          (2 * min(n1, n2)) + kappa * sqrt(p1_hat * (1 - p1_hat) / (n1 - 1) + p2_hat * (1 - p2_hat) / (n2 - 1))
        ci_lwr <- max(-1, est - term2)
        ci_upr <- min(1, est + term2)
      },
      mn = {
        .conf <- function(x1, n1, x2, n2, z, lower = FALSE) {
          p1 <- x1 / n1
          p2 <- x2 / n2
          p_hat <- p1 - p2
          dp <- 1 + ifelse(lower, 1, -1) * p_hat
          i <- 1
          while (i <= 50) {
            dp <- 0.5 * dp
            y <- p_hat + ifelse(lower, -1, 1) * dp
            score <- .score(p1, n1, p2, n2, y)
            if (score < z) {
              p_hat <- y
            }
            if ((dp < 1e-07) || (abs(z - score) < 1e-06)) {
              (break)()
            } else {
              i <- i + 1
            }
          }
          return(y)
        }
        .score <- function(p1, n1, p2, n2, dif) {
          diff <- p1 - p2 - dif
          if (abs(diff) == 0) {
            res <- 0
          } else {
            t <- n2 / n1
            a <- 1 + t
            b <- -(1 + t + p1 + t * p2 + dif * (t + 2))
            c <- dif * dif + dif * (2 * p1 + t + 1) + p1 + t * p2
            d <- -p1 * dif * (1 + dif)
            v <- (b / a / 3)^3 - b * c / (6 * a * a) + d / a / 2
            s <- sqrt((b / a / 3)^2 - c / a / 3)
            u <- ifelse(v > 0, 1, -1) * s
            w <- (3.141592654 + acos(v / u^3)) / 3
            p1d <- 2 * u * cos(w) - b / a / 3
            p2d <- p1d - dif
            n <- n1 + n2
            var <- (p1d * (1 - p1d) / n1 + p2d * (1 - p2d) / n2) * n / (n - 1)
            res <- diff^2 / var
          }
          return(res)
        }
        z <- stats::qchisq(conf.level, 1)
        ci_lwr <- max(-1, .conf(x1, n1, x2, n2, z, TRUE))
        ci_upr <- min(1, .conf(x1, n1, x2, n2, z, FALSE))
      },
      beal = {
        a <- p1_hat + p2_hat
        b <- p1_hat - p2_hat
        u <- ((1 / n1) + (1 / n2)) / 4
        v <- ((1 / n1) - (1 / n2)) / 4
        V <- u * ((2 - a) * a - b^2) + 2 * v * (1 - a) * b # nolint
        z <- stats::qchisq(p = 1 - alpha / 2, df = 1)
        A <- sqrt(z * (V + z * u^2 * (2 - a) * a + z * v^2 * (1 - a)^2)) # nolint
        B <- (b + z * v * (1 - a)) / (1 + z * u) # nolint
        ci_lwr <- max(-1, B - A / (1 + z * u))
        ci_upr <- min(1, B + A / (1 + z * u))
      },
      hal = {
        psi <- (p1_hat + p2_hat) / 2
        u <- (1 / n1 + 1 / n2) / 4
        v <- (1 / n1 - 1 / n2) / 4
        z <- kappa
        theta <- ((p1_hat - p2_hat) + z^2 * v * (1 - 2 * psi)) / (1 + z^2 * u)
        w <- z / (1 + z^2 * u) * sqrt(u * (4 * psi * (1 - psi) - (p1_hat - p2_hat)^2) + 2 * v * (1 - 2 * psi) *
          (p1_hat - p2_hat) + 4 * z^2 * u^2 * (1 - psi) * psi + z^2 * v^2 * (1 - 2 * psi)^2) # nolint
        c(theta + w, theta - w)
        ci_lwr <- max(-1, theta - w)
        ci_upr <- min(1, theta + w)
      },
      jp = {
        psi <- 0.5 * ((x1 + 0.5) / (n1 + 1) + (x2 + 0.5) / (n2 + 1))
        u <- (1 / n1 + 1 / n2) / 4
        v <- (1 / n1 - 1 / n2) / 4
        z <- kappa
        theta <- ((p1_hat - p2_hat) + z^2 * v * (1 - 2 * psi)) / (1 + z^2 * u)
        w <- z / (1 + z^2 * u) * sqrt(u * (4 * psi * (1 - psi) - (p1_hat - p2_hat)^2) + 2 * v * (1 - 2 * psi) *
          (p1_hat - p2_hat) + 4 * z^2 * u^2 * (1 - psi) * psi + z^2 * v^2 * (1 - 2 * psi)^2) # nolint
        c(theta + w, theta - w)
        ci_lwr <- max(-1, theta - w)
        ci_upr <- min(1, theta + w)
      },
    )
    ci <- c(
      est = est, lwr.ci = min(ci_lwr, ci_upr),
      upr.ci = max(ci_lwr, ci_upr)
    )
    if (sides == "left") {
      ci[3] <- 1
    } else if (sides == "right") {
      ci[2] <- -1
    }
    return(ci)
  }
  method <- match.arg(arg = method, several.ok = TRUE)
  sides <- match.arg(arg = sides, several.ok = TRUE)
  lst <- h_recycle(
    x1 = x1, n1 = n1, x2 = x2, n2 = n2, conf.level = conf.level,
    sides = sides, method = method
  )
  res <- t(sapply(1:attr(lst, "maxdim"), function(i) {
    iBinomDiffCI(
      x1 = lst$x1[i],
      n1 = lst$n1[i], x2 = lst$x2[i], n2 = lst$n2[i], conf.level = lst$conf.level[i],
      sides = lst$sides[i], method = lst$method[i]
    )
  }))
  lgn <- h_recycle(x1 = if (is.null(names(x1))) {
    paste("x1", seq_along(x1), sep = ".")
  } else {
    names(x1)
  }, n1 = if (is.null(names(n1))) {
    paste("n1", seq_along(n1), sep = ".")
  } else {
    names(n1)
  }, x2 = if (is.null(names(x2))) {
    paste("x2", seq_along(x2), sep = ".")
  } else {
    names(x2)
  }, n2 = if (is.null(names(n2))) {
    paste("n2", seq_along(n2), sep = ".")
  } else {
    names(n2)
  }, conf.level = conf.level, sides = sides, method = method)
  xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) {
    length(unique(x)) !=
      1
  })]), 1, paste, collapse = ":")
  rownames(res) <- xn
  return(res)
}

#' @describeIn desctools_binom Compute confidence intervals for binomial proportions.
#'
#' @param x (`integer(1)`)\cr number of successes.
#' @param n (`integer(1)`)\cr number of trials.
#' @param conf.level (`proportion`)\cr confidence level, defaults to 0.95.
#' @param sides (`string`)\cr side of the confidence interval to compute. Must be one of `"two-sided"` (default),
#'   `"left"`, or `"right"`.
#' @param method (`string`)\cr method to use. Can be one out of: `"wald"`, `"wilson"`, `"wilsoncc"`,
#' `"agresti-coull"`, `"jeffreys"`, `"modified wilson"`, `"modified jeffreys"`, `"clopper-pearson"`, `"arcsine"`,
#' `"logit"`, `"witting"`, `"pratt"`, `"midp"`, `"lik"`, and `"blaker"`.
#'
#' @return A `matrix` with 3 columns containing:
#'   * `est`: estimate of proportion difference.
#'   * `lwr.ci`: lower end of the confidence interval.
#'   * `upr.ci`: upper end of the confidence interval.
#'
#' @keywords internal
desctools_binomci <- function(x,
                              n,
                              conf.level = 0.95, # nolint
                              sides = c("two.sided", "left", "right"),
                              method = c(
                                "wilson", "wald", "waldcc", "agresti-coull",
                                "jeffreys", "modified wilson", "wilsoncc", "modified jeffreys",
                                "clopper-pearson", "arcsine", "logit", "witting", "pratt",
                                "midp", "lik", "blaker"
                              ),
                              rand = 123,
                              tol = 1e-05) {
  if (missing(method)) {
    method <- "wilson"
  }
  if (missing(sides)) {
    sides <- "two.sided"
  }
  iBinomCI <- function(x, n, conf.level = 0.95, sides = c("two.sided", "left", "right"), # nolint
                       method = c(
                         "wilson", "wilsoncc", "wald",
                         "waldcc", "agresti-coull", "jeffreys", "modified wilson",
                         "modified jeffreys", "clopper-pearson", "arcsine", "logit",
                         "witting", "pratt", "midp", "lik", "blaker"
                       ),
                       rand = 123,
                       tol = 1e-05) {
    if (length(x) != 1) {
      stop("'x' has to be of length 1 (number of successes)")
    }
    if (length(n) != 1) {
      stop("'n' has to be of length 1 (number of trials)")
    }
    if (length(conf.level) != 1) {
      stop("'conf.level' has to be of length 1 (confidence level)")
    }
    if (conf.level < 0.5 || conf.level > 1) {
      stop("'conf.level' has to be in [0.5, 1]")
    }
    sides <- match.arg(sides, choices = c(
      "two.sided", "left",
      "right"
    ), several.ok = FALSE)
    if (sides != "two.sided") {
      conf.level <- 1 - 2 * (1 - conf.level) # nolint
    }
    alpha <- 1 - conf.level
    kappa <- stats::qnorm(1 - alpha / 2)
    p_hat <- x / n
    q_hat <- 1 - p_hat
    est <- p_hat
    switch(match.arg(arg = method, choices = c(
      "wilson",
      "wald", "waldcc", "wilsoncc", "agresti-coull", "jeffreys",
      "modified wilson", "modified jeffreys", "clopper-pearson",
      "arcsine", "logit", "witting", "pratt", "midp", "lik",
      "blaker"
    )),
    wald = {
      term2 <- kappa * sqrt(p_hat * q_hat) / sqrt(n)
      ci_lwr <- max(0, p_hat - term2)
      ci_upr <- min(1, p_hat + term2)
    },
    waldcc = {
      term2 <- kappa * sqrt(p_hat * q_hat) / sqrt(n)
      term2 <- term2 + 1 / (2 * n)
      ci_lwr <- max(0, p_hat - term2)
      ci_upr <- min(1, p_hat + term2)
    },
    wilson = {
      term1 <- (x + kappa^2 / 2) / (n + kappa^2)
      term2 <- kappa * sqrt(n) / (n + kappa^2) * sqrt(p_hat * q_hat + kappa^2 / (4 * n))
      ci_lwr <- max(0, term1 - term2)
      ci_upr <- min(1, term1 + term2)
    },
    wilsoncc = {
      lci <- (
        2 * x + kappa^2 - 1 - kappa * sqrt(kappa^2 - 2 - 1 / n + 4 * p_hat * (n * q_hat + 1))
      ) / (2 * (n + kappa^2))
      uci <- (
        2 * x + kappa^2 + 1 + kappa * sqrt(kappa^2 + 2 - 1 / n + 4 * p_hat * (n * q_hat - 1))
      ) / (2 * (n + kappa^2))
      ci_lwr <- max(0, ifelse(p_hat == 0, 0, lci))
      ci_upr <- min(1, ifelse(p_hat == 1, 1, uci))
    },
    `agresti-coull` = {
      x_tilde <- x + kappa^2 / 2
      n_tilde <- n + kappa^2
      p_tilde <- x_tilde / n_tilde
      q_tilde <- 1 - p_tilde
      est <- p_tilde
      term2 <- kappa * sqrt(p_tilde * q_tilde) / sqrt(n_tilde)
      ci_lwr <- max(0, p_tilde - term2)
      ci_upr <- min(1, p_tilde + term2)
    },
    jeffreys = {
      if (x == 0) {
        ci_lwr <- 0
      } else {
        ci_lwr <- stats::qbeta(
          alpha / 2,
          x + 0.5, n - x + 0.5
        )
      }
      if (x == n) {
        ci_upr <- 1
      } else {
        ci_upr <- stats::qbeta(1 - alpha / 2, x + 0.5, n - x + 0.5)
      }
    },
    `modified wilson` = {
      term1 <- (x + kappa^2 / 2) / (n + kappa^2)
      term2 <- kappa * sqrt(n) / (n + kappa^2) * sqrt(p_hat * q_hat + kappa^2 / (4 * n))
      if ((n <= 50 & x %in% c(1, 2)) | (n >= 51 & x %in% c(1:3))) {
        ci_lwr <- 0.5 * stats::qchisq(alpha, 2 * x) / n
      } else {
        ci_lwr <- max(0, term1 - term2)
      }
      if ((n <= 50 & x %in% c(n - 1, n - 2)) | (n >= 51 & x %in% c(n - (1:3)))) {
        ci_upr <- 1 - 0.5 * stats::qchisq(
          alpha,
          2 * (n - x)
        ) / n
      } else {
        ci_upr <- min(1, term1 + term2)
      }
    },
    `modified jeffreys` = {
      if (x == n) {
        ci_lwr <- (alpha / 2)^(1 / n)
      } else {
        if (x <= 1) {
          ci_lwr <- 0
        } else {
          ci_lwr <- stats::qbeta(
            alpha / 2,
            x + 0.5, n - x + 0.5
          )
        }
      }
      if (x == 0) {
        ci_upr <- 1 - (alpha / 2)^(1 / n)
      } else {
        if (x >= n - 1) {
          ci_upr <- 1
        } else {
          ci_upr <- stats::qbeta(1 - alpha / 2, x + 0.5, n - x + 0.5)
        }
      }
    },
    `clopper-pearson` = {
      ci_lwr <- stats::qbeta(alpha / 2, x, n - x + 1)
      ci_upr <- stats::qbeta(1 - alpha / 2, x + 1, n - x)
    },
    arcsine = {
      p_tilde <- (x + 0.375) / (n + 0.75)
      est <- p_tilde
      ci_lwr <- sin(asin(sqrt(p_tilde)) - 0.5 * kappa / sqrt(n))^2
      ci_upr <- sin(asin(sqrt(p_tilde)) + 0.5 * kappa / sqrt(n))^2
    },
    logit = {
      lambda_hat <- log(x / (n - x))
      V_hat <- n / (x * (n - x)) # nolint
      lambda_lower <- lambda_hat - kappa * sqrt(V_hat)
      lambda_upper <- lambda_hat + kappa * sqrt(V_hat)
      ci_lwr <- exp(lambda_lower) / (1 + exp(lambda_lower))
      ci_upr <- exp(lambda_upper) / (1 + exp(lambda_upper))
    },
    witting = {
      set.seed(rand)
      x_tilde <- x + stats::runif(1, min = 0, max = 1)
      pbinom_abscont <- function(q, size, prob) {
        v <- trunc(q)
        term1 <- stats::pbinom(v - 1, size = size, prob = prob)
        term2 <- (q - v) * stats::dbinom(v, size = size, prob = prob)
        return(term1 + term2)
      }
      qbinom_abscont <- function(p, size, x) {
        fun <- function(prob, size, x, p) {
          pbinom_abscont(x, size, prob) - p
        }
        stats::uniroot(fun,
          interval = c(0, 1), size = size,
          x = x, p = p
        )$root
      }
      ci_lwr <- qbinom_abscont(1 - alpha, size = n, x = x_tilde)
      ci_upr <- qbinom_abscont(alpha, size = n, x = x_tilde)
    },
    pratt = {
      if (x == 0) {
        ci_lwr <- 0
        ci_upr <- 1 - alpha^(1 / n)
      } else if (x == 1) {
        ci_lwr <- 1 - (1 - alpha / 2)^(1 / n)
        ci_upr <- 1 - (alpha / 2)^(1 / n)
      } else if (x == (n - 1)) {
        ci_lwr <- (alpha / 2)^(1 / n)
        ci_upr <- (1 - alpha / 2)^(1 / n)
      } else if (x == n) {
        ci_lwr <- alpha^(1 / n)
        ci_upr <- 1
      } else {
        z <- stats::qnorm(1 - alpha / 2)
        A <- ((x + 1) / (n - x))^2 # nolint
        B <- 81 * (x + 1) * (n - x) - 9 * n - 8 # nolint
        C <- (0 - 3) * z * sqrt(9 * (x + 1) * (n - x) * (9 * n + 5 - z^2) + n + 1) # nolint
        D <- 81 * (x + 1)^2 - 9 * (x + 1) * (2 + z^2) + 1 # nolint
        E <- 1 + A * ((B + C) / D)^3 # nolint
        ci_upr <- 1 / E
        A <- (x / (n - x - 1))^2 # nolint
        B <- 81 * x * (n - x - 1) - 9 * n - 8 # nolint
        C <- 3 * z * sqrt(9 * x * (n - x - 1) * (9 * n + 5 - z^2) + n + 1) # nolint
        D <- 81 * x^2 - 9 * x * (2 + z^2) + 1 # nolint
        E <- 1 + A * ((B + C) / D)^3 # nolint
        ci_lwr <- 1 / E
      }
    },
    midp = {
      f_low <- function(pi, x, n) {
        1 / 2 * stats::dbinom(x, size = n, prob = pi) + stats::pbinom(x,
          size = n, prob = pi, lower.tail = FALSE
        ) -
          (1 - conf.level) / 2
      }
      f_up <- function(pi, x, n) {
        1 / 2 * stats::dbinom(x, size = n, prob = pi) + stats::pbinom(x - 1, size = n, prob = pi) - (1 - conf.level) / 2
      }
      ci_lwr <- 0
      ci_upr <- 1
      if (x != 0) {
        ci_lwr <- stats::uniroot(f_low,
          interval = c(0, p_hat),
          x = x, n = n
        )$root
      }
      if (x != n) {
        ci_upr <- stats::uniroot(f_up, interval = c(
          p_hat,
          1
        ), x = x, n = n)$root
      }
    },
    lik = {
      ci_lwr <- 0
      ci_upr <- 1
      z <- stats::qnorm(1 - alpha * 0.5)
      tol <- .Machine$double.eps^0.5
      BinDev <- function(y, x, mu, wt, bound = 0, tol = .Machine$double.eps^0.5, # nolint
                         ...) {
        ll_y <- ifelse(y %in% c(0, 1), 0, stats::dbinom(x, wt,
          y,
          log = TRUE
        ))
        ll_mu <- ifelse(mu %in% c(0, 1), 0, stats::dbinom(x,
          wt, mu,
          log = TRUE
        ))
        res <- ifelse(abs(y - mu) < tol, 0, sign(y - mu) * sqrt(-2 * (ll_y - ll_mu)))
        return(res - bound)
      }
      if (x != 0 && tol < p_hat) {
        ci_lwr <- if (BinDev(
          tol, x, p_hat, n, -z,
          tol
        ) <= 0) {
          stats::uniroot(
            f = BinDev, interval = c(tol, if (p_hat < tol || p_hat == 1) {
              1 - tol
            } else {
              p_hat
            }), bound = -z,
            x = x, mu = p_hat, wt = n
          )$root
        }
      }
      if (x != n && p_hat < (1 - tol)) {
        ci_upr <- if (
          BinDev(y = 1 - tol, x = x, mu = ifelse(p_hat > 1 - tol, tol, p_hat), wt = n, bound = z, tol = tol) < 0) { # nolint
          ci_lwr <- if (BinDev(
            tol, x, if (p_hat < tol || p_hat == 1) {
              1 - tol
            } else {
              p_hat
            }, n,
            -z, tol
          ) <= 0) {
            stats::uniroot(
              f = BinDev, interval = c(tol, p_hat),
              bound = -z, x = x, mu = p_hat, wt = n
            )$root
          }
        } else {
          stats::uniroot(
            f = BinDev, interval = c(if (p_hat > 1 - tol) {
              tol
            } else {
              p_hat
            }, 1 - tol), bound = z,
            x = x, mu = p_hat, wt = n
          )$root
        }
      }
    },
    blaker = {
      acceptbin <- function(x, n, p) {
        p1 <- 1 - stats::pbinom(x - 1, n, p)
        p2 <- stats::pbinom(x, n, p)
        a1 <- p1 + stats::pbinom(stats::qbinom(p1, n, p) - 1, n, p)
        a2 <- p2 + 1 - stats::pbinom(
          stats::qbinom(1 - p2, n, p), n,
          p
        )
        return(min(a1, a2))
      }
      ci_lwr <- 0
      ci_upr <- 1
      if (x != 0) {
        ci_lwr <- stats::qbeta((1 - conf.level) / 2, x, n - x + 1)
        while (acceptbin(x, n, ci_lwr + tol) < (1 - conf.level)) {
          ci_lwr <- ci_lwr + tol
        }
      }
      if (x != n) {
        ci_upr <- stats::qbeta(1 - (1 - conf.level) / 2, x + 1, n - x)
        while (acceptbin(x, n, ci_upr - tol) < (1 - conf.level)) {
          ci_upr <- ci_upr - tol
        }
      }
    }
    )
    ci <- c(est = est, lwr.ci = max(0, ci_lwr), upr.ci = min(
      1,
      ci_upr
    ))
    if (sides == "left") {
      ci[3] <- 1
    } else if (sides == "right") {
      ci[2] <- 0
    }
    return(ci)
  }
  lst <- list(
    x = x, n = n, conf.level = conf.level, sides = sides,
    method = method, rand = rand
  )
  maxdim <- max(unlist(lapply(lst, length)))
  lgp <- lapply(lst, rep, length.out = maxdim)
  lgn <- h_recycle(x = if (is.null(names(x))) {
    paste("x", seq_along(x), sep = ".")
  } else {
    names(x)
  }, n = if (is.null(names(n))) {
    paste("n", seq_along(n), sep = ".")
  } else {
    names(n)
  }, conf.level = conf.level, sides = sides, method = method)
  xn <- apply(as.data.frame(lgn[sapply(lgn, function(x) {
    length(unique(x)) !=
      1
  })]), 1, paste, collapse = ":")
  res <- t(sapply(1:maxdim, function(i) {
    iBinomCI(
      x = lgp$x[i],
      n = lgp$n[i], conf.level = lgp$conf.level[i], sides = lgp$sides[i],
      method = lgp$method[i], rand = lgp$rand[i]
    )
  }))
  colnames(res)[1] <- c("est")
  rownames(res) <- xn
  return(res)
}

#' Helper functions for subgroup treatment effect pattern (STEP) calculations
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions that are used internally for the STEP calculations.
#'
#' @inheritParams argument_convention
#'
#' @name h_step
#' @include control_step.R
NULL

#' @describeIn h_step Creates the windows for STEP, based on the control settings
#'   provided.
#'
#' @param x (`numeric`)\cr biomarker value(s) to use (without `NA`).
#' @param control (named `list`)\cr output from `control_step()`.
#'
#' @return
#' * `h_step_window()` returns a list containing the window-selection matrix `sel`
#'   and the interval information matrix `interval`.
#'
#' @export
h_step_window <- function(x,
                          control = control_step()) {
  checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")

  sel <- matrix(FALSE, length(x), control$num_points)
  out <- matrix(0, control$num_points, 3)
  colnames(out) <- paste("Interval", c("Center", "Lower", "Upper"))
  if (control$use_percentile) {
    # Create windows according to percentile cutoffs.
    out <- cbind(out, out)
    colnames(out)[1:3] <- paste("Percentile", c("Center", "Lower", "Upper"))
    xs <- seq(0, 1, length.out = control$num_points + 2)[-1]
    for (i in seq_len(control$num_points)) {
      out[i, 2:3] <- c(
        max(xs[i] - control$bandwidth, 0),
        min(xs[i] + control$bandwidth, 1)
      )
      out[i, 5:6] <- stats::quantile(x, out[i, 2:3])
      sel[, i] <- x >= out[i, 5] & x <= out[i, 6]
    }
    # Center is the middle point of the percentile window.
    out[, 1] <- xs[-control$num_points - 1]
    out[, 4] <- stats::quantile(x, out[, 1])
  } else {
    # Create windows according to cutoffs.
    m <- c(min(x), max(x))
    xs <- seq(m[1], m[2], length.out = control$num_points + 2)[-1]
    for (i in seq_len(control$num_points)) {
      out[i, 2:3] <- c(
        max(xs[i] - control$bandwidth, m[1]),
        min(xs[i] + control$bandwidth, m[2])
      )
      sel[, i] <- x >= out[i, 2] & x <= out[i, 3]
    }
    # Center is the same as the point for predicting.
    out[, 1] <- xs[-control$num_points - 1]
  }
  list(sel = sel, interval = out)
}

#' @describeIn h_step Calculates the estimated treatment effect estimate
#'   on the linear predictor scale and corresponding standard error from a STEP `model` fitted
#'   on `data` given `variables` specification, for a single biomarker value `x`.
#'   This works for both `coxph` and `glm` models, i.e. for calculating log hazard ratio or log odds
#'   ratio estimates.
#'
#' @param model (`coxph` or `glm`)\cr the regression model object.
#'
#' @return
#' * `h_step_trt_effect()` returns a vector with elements `est` and `se`.
#'
#' @export
h_step_trt_effect <- function(data,
                              model,
                              variables,
                              x) {
  checkmate::assert_multi_class(model, c("coxph", "glm"))
  checkmate::assert_number(x)
  assert_df_with_variables(data, variables)
  checkmate::assert_factor(data[[variables$arm]], n.levels = 2)

  newdata <- data[c(1, 1), ]
  newdata[, variables$biomarker] <- x
  newdata[, variables$arm] <- levels(data[[variables$arm]])
  model_terms <- stats::delete.response(stats::terms(model))
  model_frame <- stats::model.frame(model_terms, data = newdata, xlev = model$xlevels)
  mat <- stats::model.matrix(model_terms, data = model_frame, contrasts.arg = model$contrasts)
  coefs <- stats::coef(model)
  # Note: It is important to use the coef subset from matrix, otherwise intercept and
  # strata are included for coxph() models.
  mat <- mat[, names(coefs)]
  mat_diff <- diff(mat)
  est <- mat_diff %*% coefs
  var <- mat_diff %*% stats::vcov(model) %*% t(mat_diff)
  se <- sqrt(var)
  c(
    est = est,
    se = se
  )
}

#' @describeIn h_step Builds the model formula used in survival STEP calculations.
#'
#' @return
#' * `h_step_survival_formula()` returns a model formula.
#'
#' @export
h_step_survival_formula <- function(variables,
                                    control = control_step()) {
  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  assert_list_of_variables(variables[c("arm", "biomarker", "event", "time")])
  form <- paste0("Surv(", variables$time, ", ", variables$event, ") ~ ", variables$arm)
  if (control$degree > 0) {
    form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
  }
  if (!is.null(variables$covariates)) {
    form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
  }
  if (!is.null(variables$strata)) {
    form <- paste0(form, " + strata(", paste0(variables$strata, collapse = ", "), ")")
  }
  stats::as.formula(form)
}

#' @describeIn h_step Estimates the model with `formula` built based on
#'   `variables` in `data` for a given `subset` and `control` parameters for the
#'   Cox regression.
#'
#' @param formula (`formula`)\cr the regression model formula.
#' @param subset (`logical`)\cr subset vector.
#'
#' @return
#' * `h_step_survival_est()` returns a matrix of number of observations `n`,
#'   `events`, log hazard ratio estimates `loghr`, standard error `se`,
#'   and Wald confidence interval bounds `ci_lower` and `ci_upper`. One row is
#'   included for each biomarker value in `x`.
#'
#' @export
h_step_survival_est <- function(formula,
                                data,
                                variables,
                                x,
                                subset = rep(TRUE, nrow(data)),
                                control = control_coxph()) {
  checkmate::assert_formula(formula)
  assert_df_with_variables(data, variables)
  checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")

  # Note: `subset` in `coxph` needs to be an expression referring to `data` variables.
  data$.subset <- subset
  coxph_warnings <- NULL
  tryCatch(
    withCallingHandlers(
      expr = {
        fit <- survival::coxph(
          formula = formula,
          data = data,
          subset = .subset,
          ties = control$ties
        )
      },
      warning = function(w) {
        coxph_warnings <<- c(coxph_warnings, w)
        invokeRestart("muffleWarning")
      }
    ),
    finally = {
    }
  )
  if (!is.null(coxph_warnings)) {
    warning(paste(
      "Fit warnings occurred, please consider using a simpler model, or",
      "larger `bandwidth`, less `num_points` in `control_step()` settings"
    ))
  }
  # Produce a matrix with one row per `x` and columns `est` and `se`.
  estimates <- t(vapply(
    X = x,
    FUN = h_step_trt_effect,
    FUN.VALUE = c(1, 2),
    data = data,
    model = fit,
    variables = variables
  ))
  q_norm <- stats::qnorm((1 + control$conf_level) / 2)
  cbind(
    n = fit$n,
    events = fit$nevent,
    loghr = estimates[, "est"],
    se = estimates[, "se"],
    ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
    ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
  )
}

#' @describeIn h_step Builds the model formula used in response STEP calculations.
#'
#' @return
#' * `h_step_rsp_formula()` returns a model formula.
#'
#' @export
h_step_rsp_formula <- function(variables,
                               control = c(control_step(), control_logistic())) {
  checkmate::assert_character(variables$covariates, null.ok = TRUE)
  assert_list_of_variables(variables[c("arm", "biomarker", "response")])
  response_definition <- sub(
    pattern = "response",
    replacement = variables$response,
    x = control$response_definition,
    fixed = TRUE
  )
  form <- paste0(response_definition, " ~ ", variables$arm)
  if (control$degree > 0) {
    form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
  }
  if (!is.null(variables$covariates)) {
    form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
  }
  if (!is.null(variables$strata)) {
    strata_arg <- if (length(variables$strata) > 1) {
      paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
    } else {
      variables$strata
    }
    form <- paste0(form, "+ strata(", strata_arg, ")")
  }
  stats::as.formula(form)
}

#' @describeIn h_step Estimates the model with `formula` built based on
#'   `variables` in `data` for a given `subset` and `control` parameters for the
#'   logistic regression.
#'
#' @param formula (`formula`)\cr the regression model formula.
#' @param subset (`logical`)\cr subset vector.
#'
#' @return
#' * `h_step_rsp_est()` returns a matrix of number of observations `n`, log odds
#'   ratio estimates `logor`, standard error `se`, and Wald confidence interval bounds
#'   `ci_lower` and `ci_upper`. One row is included for each biomarker value in `x`.
#'
#' @export
h_step_rsp_est <- function(formula,
                           data,
                           variables,
                           x,
                           subset = rep(TRUE, nrow(data)),
                           control = control_logistic()) {
  checkmate::assert_formula(formula)
  assert_df_with_variables(data, variables)
  checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")
  # Note: `subset` in `glm` needs to be an expression referring to `data` variables.
  data$.subset <- subset
  fit_warnings <- NULL
  tryCatch(
    withCallingHandlers(
      expr = {
        fit <- if (is.null(variables$strata)) {
          stats::glm(
            formula = formula,
            data = data,
            subset = .subset,
            family = stats::binomial("logit")
          )
        } else {
          # clogit needs coxph and strata imported
          survival::clogit(
            formula = formula,
            data = data,
            subset = .subset
          )
        }
      },
      warning = function(w) {
        fit_warnings <<- c(fit_warnings, w)
        invokeRestart("muffleWarning")
      }
    ),
    finally = {
    }
  )
  if (!is.null(fit_warnings)) {
    warning(paste(
      "Fit warnings occurred, please consider using a simpler model, or",
      "larger `bandwidth`, less `num_points` in `control_step()` settings"
    ))
  }
  # Produce a matrix with one row per `x` and columns `est` and `se`.
  estimates <- t(vapply(
    X = x,
    FUN = h_step_trt_effect,
    FUN.VALUE = c(1, 2),
    data = data,
    model = fit,
    variables = variables
  ))
  q_norm <- stats::qnorm((1 + control$conf_level) / 2)
  cbind(
    n = length(fit$y),
    logor = estimates[, "est"],
    se = estimates[, "se"],
    ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
    ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
  )
}

#' Control functions for Kaplan-Meier plot annotation tables
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Auxiliary functions for controlling arguments for formatting the annotation tables that can be added to plots
#' generated via [g_km()].
#'
#' @param x (`proportion`)\cr x-coordinate for center of annotation table.
#' @param y (`proportion`)\cr y-coordinate for center of annotation table.
#' @param w (`proportion`)\cr relative width of the annotation table.
#' @param h (`proportion`)\cr relative height of the annotation table.
#' @param fill (`flag` or `character`)\cr whether the annotation table should have a background fill color.
#'   Can also be a color code to use as the background fill color. If `TRUE`, color code defaults to `"#00000020"`.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @seealso [g_km()]
#'
#' @name control_annot
NULL

#' @describeIn control_annot Control function for formatting the median survival time annotation table. This annotation
#'   table can be added in [g_km()] by setting `annot_surv_med=TRUE`, and can be configured using the
#'   `control_surv_med_annot()` function by setting it as the `control_annot_surv_med` argument.
#'
#' @examples
#' control_surv_med_annot()
#'
#' @export
control_surv_med_annot <- function(x = 0.8, y = 0.85, w = 0.32, h = 0.16, fill = TRUE) {
  assert_proportion_value(x)
  assert_proportion_value(y)
  assert_proportion_value(w)
  assert_proportion_value(h)

  list(x = x, y = y, w = w, h = h, fill = fill)
}

#' @describeIn control_annot Control function for formatting the Cox-PH annotation table. This annotation table can be
#'   added in [g_km()] by setting `annot_coxph=TRUE`, and can be configured using the `control_coxph_annot()` function
#'   by setting it as the `control_annot_coxph` argument.
#'
#' @param ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the
#'   annotation table. If `FALSE` (default), only comparison groups will be printed in the table labels.
#'
#' @examples
#' control_coxph_annot()
#'
#' @export
control_coxph_annot <- function(x = 0.29, y = 0.51, w = 0.4, h = 0.125, fill = TRUE, ref_lbls = FALSE) {
  checkmate::assert_logical(ref_lbls, any.missing = FALSE)

  res <- c(control_surv_med_annot(x = x, y = y, w = w, h = h), list(ref_lbls = ref_lbls))
  res
}

#' Helper function to calculate x-tick positions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Calculate the positions of ticks on the x-axis. However, if `xticks` already
#' exists it is kept as is. It is based on the same function `ggplot2` relies on,
#' and is required in the graphic and the patient-at-risk annotation table.
#'
#' @inheritParams g_km
#' @inheritParams h_ggkm
#'
#' @return A vector of positions to use for x-axis ticks on a `ggplot` object.
#'
#' @examples
#' library(dplyr)
#' library(survival)
#'
#' data <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_data_plot()
#'
#' h_xticks(data)
#' h_xticks(data, xticks = seq(0, 3000, 500))
#' h_xticks(data, xticks = 500)
#' h_xticks(data, xticks = 500, max_time = 6000)
#' h_xticks(data, xticks = c(0, 500), max_time = 300)
#' h_xticks(data, xticks = 500, max_time = 300)
#'
#' @export
h_xticks <- function(data, xticks = NULL, max_time = NULL) {
  if (is.null(xticks)) {
    if (is.null(max_time)) {
      labeling::extended(range(data$time)[1], range(data$time)[2], m = 5)
    } else {
      labeling::extended(range(data$time)[1], max(range(data$time)[2], max_time), m = 5)
    }
  } else if (checkmate::test_number(xticks)) {
    if (is.null(max_time)) {
      seq(0, max(data$time), xticks)
    } else {
      seq(0, max(data$time, max_time), xticks)
    }
  } else if (is.numeric(xticks)) {
    xticks
  } else {
    stop(
      paste(
        "xticks should be either `NULL`",
        "or a single number (interval between x ticks)",
        "or a numeric vector (position of ticks on the x axis)"
      )
    )
  }
}

#' Helper function for survival estimations
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Transform a survival fit to a table with groups in rows characterized by N, median and confidence interval.
#'
#' @inheritParams h_data_plot
#'
#' @return A summary table with statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).
#'
#' @examples
#' library(dplyr)
#' library(survival)
#'
#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "OS")
#' fit <- survfit(
#'   formula = Surv(AVAL, 1 - CNSR) ~ ARMCD,
#'   data = adtte
#' )
#' h_tbl_median_surv(fit_km = fit)
#'
#' @export
h_tbl_median_surv <- function(fit_km, armval = "All") {
  y <- if (is.null(fit_km$strata)) {
    as.data.frame(t(summary(fit_km)$table), row.names = armval)
  } else {
    tbl <- summary(fit_km)$table
    rownames_lst <- strsplit(sub("=", "equals", rownames(tbl)), "equals")
    rownames(tbl) <- matrix(unlist(rownames_lst), ncol = 2, byrow = TRUE)[, 2]
    as.data.frame(tbl)
  }
  conf.int <- summary(fit_km)$conf.int # nolint
  y$records <- round(y$records)
  y$median <- signif(y$median, 4)
  y$`CI` <- paste0(
    "(", signif(y[[paste0(conf.int, "LCL")]], 4), ", ", signif(y[[paste0(conf.int, "UCL")]], 4), ")"
  )
  stats::setNames(
    y[c("records", "median", "CI")],
    c("N", "Median", f_conf_level(conf.int))
  )
}

#' Helper function for generating a pairwise Cox-PH table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Create a `data.frame` of pairwise stratified or unstratified Cox-PH analysis results.
#'
#' @inheritParams g_km
#' @param annot_coxph_ref_lbls (`flag`)\cr whether the reference group should be explicitly printed in labels for the
#'   `annot_coxph` table. If `FALSE` (default), only comparison groups will be printed in `annot_coxph` table labels.
#'
#' @return A `data.frame` containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),
#'   and `p-value (log-rank)`.
#'
#' @examples
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#'
#' h_tbl_coxph_pairwise(
#'   df = adtte,
#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM"),
#'   control_coxph_pw = control_coxph(conf_level = 0.9)
#' )
#'
#' @export
h_tbl_coxph_pairwise <- function(df,
                                 variables,
                                 ref_group_coxph = NULL,
                                 control_coxph_pw = control_coxph(),
                                 annot_coxph_ref_lbls = FALSE) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_tbl_coxph_pairwise() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  assert_df_with_variables(df, variables)
  checkmate::assert_choice(ref_group_coxph, levels(df[[variables$arm]]), null.ok = TRUE)
  checkmate::assert_flag(annot_coxph_ref_lbls)

  arm <- variables$arm
  df[[arm]] <- factor(df[[arm]])

  ref_group <- if (!is.null(ref_group_coxph)) ref_group_coxph else levels(df[[variables$arm]])[1]
  comp_group <- setdiff(levels(df[[arm]]), ref_group)

  results <- Map(function(comp) {
    res <- s_coxph_pairwise(
      df = df[df[[arm]] == comp, , drop = FALSE],
      .ref_group = df[df[[arm]] == ref_group, , drop = FALSE],
      .in_ref_col = FALSE,
      .var = variables$tte,
      is_event = variables$is_event,
      strata = variables$strata,
      control = control_coxph_pw
    )
    res_df <- data.frame(
      hr = format(round(res$hr, 2), nsmall = 2),
      hr_ci = paste0(
        "(", format(round(res$hr_ci[1], 2), nsmall = 2), ", ",
        format(round(res$hr_ci[2], 2), nsmall = 2), ")"
      ),
      pvalue = if (res$pvalue < 0.0001) "<0.0001" else format(round(res$pvalue, 4), 4),
      stringsAsFactors = FALSE
    )
    colnames(res_df) <- c("HR", vapply(res[c("hr_ci", "pvalue")], obj_label, FUN.VALUE = "character"))
    row.names(res_df) <- comp
    res_df
  }, comp_group)
  if (annot_coxph_ref_lbls) names(results) <- paste(comp_group, "vs.", ref_group)

  do.call(rbind, results)
}

#' Helper function to tidy survival fit data
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convert the survival fit data into a data frame designed for plotting
#' within `g_km`.
#'
#' This starts from the [broom::tidy()] result, and then:
#'   * Post-processes the `strata` column into a factor.
#'   * Extends each stratum by an additional first row with time 0 and probability 1 so that
#'     downstream plot lines start at those coordinates.
#'   * Adds a `censor` column.
#'   * Filters the rows before `max_time`.
#'
#' @inheritParams g_km
#' @param fit_km (`survfit`)\cr result of [survival::survfit()].
#' @param armval (`string`)\cr used as strata name when treatment arm variable only has one level. Default is `"All"`.
#'
#' @return A `tibble` with columns `time`, `n.risk`, `n.event`, `n.censor`, `estimate`, `std.error`, `conf.high`,
#'   `conf.low`, `strata`, and `censor`.
#'
#' @examples
#' library(dplyr)
#' library(survival)
#'
#' # Test with multiple arms
#' tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_data_plot()
#'
#' # Test with single arm
#' tern_ex_adtte %>%
#'   filter(PARAMCD == "OS", ARMCD == "ARM B") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_data_plot(armval = "ARM B")
#'
#' @export
h_data_plot <- function(fit_km,
                        armval = "All",
                        max_time = NULL) {
  y <- broom::tidy(fit_km)

  if (!is.null(fit_km$strata)) {
    fit_km_var_level <- strsplit(sub("=", "equals", names(fit_km$strata)), "equals")
    strata_levels <- vapply(fit_km_var_level, FUN = "[", FUN.VALUE = "a", i = 2)
    strata_var_level <- strsplit(sub("=", "equals", y$strata), "equals")
    y$strata <- factor(
      vapply(strata_var_level, FUN = "[", FUN.VALUE = "a", i = 2),
      levels = strata_levels
    )
  } else {
    y$strata <- armval
  }

  y_by_strata <- split(y, y$strata)
  y_by_strata_extended <- lapply(
    y_by_strata,
    FUN = function(tbl) {
      first_row <- tbl[1L, ]
      first_row$time <- 0
      first_row$n.risk <- sum(first_row[, c("n.risk", "n.event", "n.censor")])
      first_row$n.event <- first_row$n.censor <- 0
      first_row$estimate <- first_row$conf.high <- first_row$conf.low <- 1
      first_row$std.error <- 0
      rbind(
        first_row,
        tbl
      )
    }
  )
  y <- do.call(rbind, y_by_strata_extended)

  y$censor <- ifelse(y$n.censor > 0, y$estimate, NA)
  if (!is.null(max_time)) {
    y <- y[y$time <= max(max_time), ]
  }
  y
}

## Deprecated Functions ----

#' Helper function to create a KM plot
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Draw the Kaplan-Meier plot using `ggplot2`.
#'
#' @inheritParams g_km
#' @param data (`data.frame`)\cr survival data as pre-processed by `h_data_plot`.
#'
#' @return A `ggplot` object.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks,
#'   xlab = "Days",
#'   yval = "Survival",
#'   ylab = "Survival Probability",
#'   title = "Survival"
#' )
#' gg
#' }
#'
#' @export
h_ggkm <- function(data,
                   xticks = NULL,
                   yval = "Survival",
                   censor_show,
                   xlab,
                   ylab,
                   ylim = NULL,
                   title,
                   footnotes = NULL,
                   max_time = NULL,
                   lwd = 1,
                   lty = NULL,
                   pch = 3,
                   size = 2,
                   col = NULL,
                   ci_ribbon = FALSE,
                   ggtheme = nestcolor::theme_nest()) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_ggkm()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  checkmate::assert_numeric(lty, null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)

  if (is.null(ylim)) {
    data_lims <- data
    if (yval == "Failure") data_lims[["estimate"]] <- 1 - data_lims[["estimate"]]
    if (!is.null(max_time)) {
      y_lwr <- min(data_lims[data_lims$time < max_time, ][["estimate"]])
      y_upr <- max(data_lims[data_lims$time < max_time, ][["estimate"]])
    } else {
      y_lwr <- min(data_lims[["estimate"]])
      y_upr <- max(data_lims[["estimate"]])
    }
    ylim <- c(y_lwr, y_upr)
  }
  checkmate::assert_numeric(ylim, finite = TRUE, any.missing = FALSE, len = 2, sorted = TRUE)

  # change estimates of survival to estimates of failure (1 - survival)
  if (yval == "Failure") {
    data$estimate <- 1 - data$estimate
    data[c("conf.high", "conf.low")] <- list(1 - data$conf.low, 1 - data$conf.high)
    data$censor <- 1 - data$censor
  }

  gg <- {
    ggplot2::ggplot(
      data = data,
      mapping = ggplot2::aes(
        x = .data[["time"]],
        y = .data[["estimate"]],
        ymin = .data[["conf.low"]],
        ymax = .data[["conf.high"]],
        color = .data[["strata"]],
        fill = .data[["strata"]]
      )
    ) +
      ggplot2::geom_hline(yintercept = 0)
  }

  if (ci_ribbon) {
    gg <- gg + ggplot2::geom_ribbon(alpha = .3, lty = 0)
  }

  gg <- if (is.null(lty)) {
    gg +
      ggplot2::geom_step(linewidth = lwd)
  } else if (checkmate::test_number(lty)) {
    gg +
      ggplot2::geom_step(linewidth = lwd, lty = lty)
  } else if (is.numeric(lty)) {
    gg +
      ggplot2::geom_step(mapping = ggplot2::aes(linetype = .data[["strata"]]), linewidth = lwd) +
      ggplot2::scale_linetype_manual(values = lty)
  }

  gg <- gg +
    ggplot2::coord_cartesian(ylim = ylim) +
    ggplot2::labs(x = xlab, y = ylab, title = title, caption = footnotes)

  if (!is.null(col)) {
    gg <- gg +
      ggplot2::scale_color_manual(values = col) +
      ggplot2::scale_fill_manual(values = col)
  }
  if (censor_show) {
    dt <- data[data$n.censor != 0, ]
    dt$censor_lbl <- factor("Censored")

    gg <- gg + ggplot2::geom_point(
      data = dt,
      ggplot2::aes(
        x = .data[["time"]],
        y = .data[["censor"]],
        shape = .data[["censor_lbl"]]
      ),
      size = size,
      show.legend = TRUE,
      inherit.aes = TRUE
    ) +
      ggplot2::scale_shape_manual(name = NULL, values = pch) +
      ggplot2::guides(
        shape = ggplot2::guide_legend(override.aes = list(linetype = NA)),
        fill = ggplot2::guide_legend(override.aes = list(shape = NA))
      )
  }

  if (!is.null(max_time) && !is.null(xticks)) {
    gg <- gg + ggplot2::scale_x_continuous(breaks = xticks, limits = c(min(0, xticks), max(c(xticks, max_time))))
  } else if (!is.null(xticks)) {
    if (max(data$time) <= max(xticks)) {
      gg <- gg + ggplot2::scale_x_continuous(breaks = xticks, limits = c(min(0, min(xticks)), max(xticks)))
    } else {
      gg <- gg + ggplot2::scale_x_continuous(breaks = xticks)
    }
  } else if (!is.null(max_time)) {
    gg <- gg + ggplot2::scale_x_continuous(limits = c(0, max_time))
  }

  if (!is.null(ggtheme)) {
    gg <- gg + ggtheme
  }

  gg + ggplot2::theme(
    legend.position = "bottom",
    legend.title = ggplot2::element_blank(),
    legend.key.height = unit(0.02, "npc"),
    panel.grid.major.x = ggplot2::element_line(linewidth = 2)
  )
}

#' `ggplot` decomposition
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' The elements composing the `ggplot` are extracted and organized in a `list`.
#'
#' @param gg (`ggplot`)\cr a graphic to decompose.
#'
#' @return A named `list` with elements:
#'   * `panel`: The panel.
#'   * `yaxis`: The y-axis.
#'   * `xaxis`: The x-axis.
#'   * `xlab`: The x-axis label.
#'   * `ylab`: The y-axis label.
#'   * `guide`: The legend.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   yval = "Survival",
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "tt",
#'   footnotes = "ff"
#' )
#'
#' g_el <- h_decompose_gg(gg)
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "red", fill = "gray85", lwd = 5))
#' grid::grid.draw(g_el$panel)
#'
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "royalblue", fill = "gray85", lwd = 5))
#' grid::grid.draw(with(g_el, cbind(ylab, yaxis)))
#' }
#'
#' @export
h_decompose_gg <- function(gg) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_decompose_gg()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  g_el <- ggplot2::ggplotGrob(gg)
  y <- c(
    panel = "panel",
    yaxis = "axis-l",
    xaxis = "axis-b",
    xlab = "xlab-b",
    ylab = "ylab-l",
    guide = "guide"
  )
  lapply(X = y, function(x) gtable::gtable_filter(g_el, x))
}

#' Helper function to prepare a KM layout
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Prepares a (5 rows) x (2 cols) layout for the Kaplan-Meier curve.
#'
#' @inheritParams g_km
#' @inheritParams h_ggkm
#' @param g_el (`list` of `gtable`)\cr list as obtained by `h_decompose_gg()`.
#' @param annot_at_risk (`flag`)\cr compute and add the annotation table reporting the number of
#'   patient at risk matching the main grid of the Kaplan-Meier curve.
#'
#' @return A grid layout.
#'
#' @details The layout corresponds to a grid of two columns and five rows of unequal dimensions. Most of the
#'   dimension are fixed, only the curve is flexible and will accommodate with the remaining free space.
#'   * The left column gets the annotation of the `ggplot` (y-axis) and the names of the strata for the patient
#'     at risk tabulation. The main constraint is about the width of the columns which must allow the writing of
#'     the strata name.
#'   * The right column receive the `ggplot`, the legend, the x-axis and the patient at risk table.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "tt", footnotes = "ff", yval = "Survival"
#' )
#' g_el <- h_decompose_gg(gg)
#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")
#' grid.show.layout(lyt)
#' }
#'
#' @export
h_km_layout <- function(data, g_el, title, footnotes, annot_at_risk = TRUE, annot_at_risk_title = TRUE) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_km_layout()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  txtlines <- levels(as.factor(data$strata))
  nlines <- nlevels(as.factor(data$strata))
  col_annot_width <- max(
    c(
      as.numeric(grid::convertX(g_el$yaxis$widths + g_el$ylab$widths, "pt")),
      as.numeric(
        grid::convertX(
          grid::stringWidth(txtlines) + grid::unit(7, "pt"), "pt"
        )
      )
    )
  )

  ttl_row <- as.numeric(!is.null(title))
  foot_row <- as.numeric(!is.null(footnotes))
  no_tbl_ind <- c()
  ht_x <- c()
  ht_units <- c()

  if (ttl_row == 1) {
    no_tbl_ind <- c(no_tbl_ind, TRUE)
    ht_x <- c(ht_x, 2)
    ht_units <- c(ht_units, "lines")
  }

  no_tbl_ind <- c(no_tbl_ind, rep(TRUE, 3), rep(FALSE, 2))
  ht_x <- c(
    ht_x,
    1,
    grid::convertX(with(g_el, xaxis$heights + ylab$widths), "pt") + grid::unit(5, "pt"),
    grid::convertX(g_el$guide$heights, "pt") + grid::unit(2, "pt"),
    1,
    nlines + 0.5,
    grid::convertX(with(g_el, xaxis$heights + ylab$widths), "pt")
  )
  ht_units <- c(
    ht_units,
    "null",
    "pt",
    "pt",
    "lines",
    "lines",
    "pt"
  )

  if (foot_row == 1) {
    no_tbl_ind <- c(no_tbl_ind, TRUE)
    ht_x <- c(ht_x, 1)
    ht_units <- c(ht_units, "lines")
  }
  if (annot_at_risk) {
    no_at_risk_tbl <- rep(TRUE, 6 + ttl_row + foot_row)
    if (!annot_at_risk_title) {
      no_at_risk_tbl[length(no_at_risk_tbl) - 2 - foot_row] <- FALSE
    }
  } else {
    no_at_risk_tbl <- no_tbl_ind
  }

  grid::grid.layout(
    nrow = sum(no_at_risk_tbl), ncol = 2,
    widths = grid::unit(c(col_annot_width, 1), c("pt", "null")),
    heights = grid::unit(
      x = ht_x[no_at_risk_tbl],
      units = ht_units[no_at_risk_tbl]
    )
  )
}

#' Helper function to create patient-at-risk grobs
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Two graphical objects are obtained, one corresponding to row labeling and the second to the table of
#' numbers of patients at risk. If `title = TRUE`, a third object corresponding to the table title is
#' also obtained.
#'
#' @inheritParams g_km
#' @inheritParams h_ggkm
#' @param annot_tbl (`data.frame`)\cr annotation as prepared by [survival::summary.survfit()] which
#'   includes the number of patients at risk at given time points.
#' @param xlim (`numeric(1)`)\cr the maximum value on the x-axis (used to ensure the at risk table aligns with the KM
#'   graph).
#' @param title (`flag`)\cr whether the "Patients at Risk" title should be added above the `annot_at_risk`
#'   table. Has no effect if `annot_at_risk` is `FALSE`. Defaults to `TRUE`.
#'
#' @return A named `list` of two `gTree` objects if `title = FALSE`: `at_risk` and `label`, or three
#'   `gTree` objects if `title = TRUE`: `at_risk`, `label`, and `title`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#'
#' data_plot <- h_data_plot(fit_km = fit_km)
#'
#' xticks <- h_xticks(data = data_plot)
#'
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "tt", footnotes = "ff", yval = "Survival"
#' )
#'
#' # The annotation table reports the patient at risk for a given strata and
#' # times (`xticks`).
#' annot_tbl <- summary(fit_km, times = xticks)
#' if (is.null(fit_km$strata)) {
#'   annot_tbl <- with(annot_tbl, data.frame(n.risk = n.risk, time = time, strata = "All"))
#' } else {
#'   strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")
#'   levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]
#'   annot_tbl <- data.frame(
#'     n.risk = annot_tbl$n.risk,
#'     time = annot_tbl$time,
#'     strata = annot_tbl$strata
#'   )
#' }
#'
#' # The annotation table is transformed into a grob.
#' tbl <- h_grob_tbl_at_risk(data = data_plot, annot_tbl = annot_tbl, xlim = max(xticks))
#'
#' # For the representation, the layout is estimated for which the decomposition
#' # of the graphic element is necessary.
#' g_el <- h_decompose_gg(gg)
#' lyt <- h_km_layout(data = data_plot, g_el = g_el, title = "t", footnotes = "f")
#'
#' grid::grid.newpage()
#' pushViewport(viewport(layout = lyt, height = .95, width = .95))
#' grid.rect(gp = grid::gpar(lty = 1, col = "purple", fill = "gray85", lwd = 1))
#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 2))
#' grid.rect(gp = grid::gpar(lty = 1, col = "orange", fill = "gray85", lwd = 1))
#' grid::grid.draw(tbl$at_risk)
#' popViewport()
#' pushViewport(viewport(layout.pos.row = 3:4, layout.pos.col = 1))
#' grid.rect(gp = grid::gpar(lty = 1, col = "green3", fill = "gray85", lwd = 1))
#' grid::grid.draw(tbl$label)
#' }
#'
#' @export
h_grob_tbl_at_risk <- function(data, annot_tbl, xlim, title = TRUE) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_grob_tbl_at_risk()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  txtlines <- levels(as.factor(data$strata))
  nlines <- nlevels(as.factor(data$strata))
  y_int <- annot_tbl$time[2] - annot_tbl$time[1]
  annot_tbl <- expand.grid(
    time = seq(0, xlim, y_int),
    strata = unique(annot_tbl$strata)
  ) %>% dplyr::left_join(annot_tbl, by = c("time", "strata"))
  annot_tbl[is.na(annot_tbl)] <- 0
  y_str_unit <- as.numeric(annot_tbl$strata)
  vp_table <- grid::plotViewport(margins = grid::unit(c(0, 0, 0, 0), "lines"))
  if (title) {
    gb_table_title <- grid::gList(
      grid::textGrob(
        label = "Patients at Risk:",
        x = 1,
        y = grid::unit(0.2, "native"),
        gp = grid::gpar(fontface = "bold", fontsize = 10)
      )
    )
  }
  gb_table_left_annot <- grid::gList(
    grid::rectGrob(
      x = 0, y = grid::unit(c(1:nlines) - 1, "lines"),
      gp = grid::gpar(fill = c("gray95", "gray90"), alpha = 1, col = "white"),
      height = grid::unit(1, "lines"), just = "bottom", hjust = 0
    ),
    grid::textGrob(
      label = unique(annot_tbl$strata),
      x = 0.5,
      y = grid::unit(
        (max(unique(y_str_unit)) - unique(y_str_unit)) + 0.75,
        "native"
      ),
      gp = grid::gpar(fontface = "italic", fontsize = 10)
    )
  )
  gb_patient_at_risk <- grid::gList(
    grid::rectGrob(
      x = 0, y = grid::unit(c(1:nlines) - 1, "lines"),
      gp = grid::gpar(fill = c("gray95", "gray90"), alpha = 1, col = "white"),
      height = grid::unit(1, "lines"), just = "bottom", hjust = 0
    ),
    grid::textGrob(
      label = annot_tbl$n.risk,
      x = grid::unit(annot_tbl$time, "native"),
      y = grid::unit(
        (max(y_str_unit) - y_str_unit) + .5,
        "line"
      ) # maybe native
    )
  )

  ret <- list(
    at_risk = grid::gList(
      grid::gTree(
        vp = vp_table,
        children = grid::gList(
          grid::gTree(
            vp = grid::dataViewport(
              xscale = c(0, xlim) + c(-0.05, 0.05) * xlim,
              yscale = c(0, nlines + 1),
              extension = c(0.05, 0)
            ),
            children = grid::gList(gb_patient_at_risk)
          )
        )
      )
    ),
    label = grid::gList(
      grid::gTree(
        vp = grid::viewport(width = max(grid::stringWidth(txtlines))),
        children = grid::gList(
          grid::gTree(
            vp = grid::dataViewport(
              xscale = 0:1,
              yscale = c(0, nlines + 1),
              extension = c(0.0, 0)
            ),
            children = grid::gList(gb_table_left_annot)
          )
        )
      )
    )
  )

  if (title) {
    ret[["title"]] <- grid::gList(
      grid::gTree(
        vp = grid::viewport(width = max(grid::stringWidth(txtlines))),
        children = grid::gList(
          grid::gTree(
            vp = grid::dataViewport(
              xscale = 0:1,
              yscale = c(0, 1),
              extension = c(0, 0)
            ),
            children = grid::gList(gb_table_title)
          )
        )
      )
    )
  }

  ret
}

#' Helper function to create survival estimation grobs
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' The survival fit is transformed in a grob containing a table with groups in
#' rows characterized by N, median and 95% confidence interval.
#'
#' @inheritParams g_km
#' @inheritParams h_data_plot
#' @param ttheme (`list`)\cr see [gridExtra::ttheme_default()].
#' @param x (`proportion`)\cr a value between 0 and 1 specifying x-location.
#' @param y (`proportion`)\cr a value between 0 and 1 specifying y-location.
#' @param width (`grid::unit`)\cr width (as a unit) to use when printing the grob.
#'
#' @return A `grob` of a table containing statistics `N`, `Median`, and `XX% CI` (`XX` taken from `fit_km`).
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))
#' tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .) %>%
#'   h_grob_median_surv() %>%
#'   grid::grid.draw()
#' }
#'
#' @export
h_grob_median_surv <- function(fit_km,
                               armval = "All",
                               x = 0.9,
                               y = 0.9,
                               width = grid::unit(0.3, "npc"),
                               ttheme = gridExtra::ttheme_default()) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_grob_median_surv()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  data <- h_tbl_median_surv(fit_km, armval = armval)

  width <- grid::convertUnit(grid::unit(as.numeric(width), grid::unitType(width)), "in")
  height <- width * (nrow(data) + 1) / 12

  w <- paste(" ", c(
    rownames(data)[which.max(nchar(rownames(data)))],
    sapply(names(data), function(x) c(x, data[[x]])[which.max(nchar(c(x, data[[x]])))])
  ))
  w_unit <- grid::convertWidth(grid::stringWidth(w), "in", valueOnly = TRUE)

  w_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strwidth(w[4], units = "in")
  })
  f_size_w <- which.max(w_txt[w_txt < as.numeric((w_unit / sum(w_unit)) * width)[4]])

  h_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strheight(grid::stringHeight("X"), units = "in")
  })
  f_size_h <- which.max(h_txt[h_txt < as.numeric(grid::unit(as.numeric(height) / 4, grid::unitType(height)))])

  if (ttheme$core$fg_params$fontsize == 12) {
    ttheme$core$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$colhead$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$rowhead$fg_params$fontsize <- min(f_size_w, f_size_h)
  }

  gt <- gridExtra::tableGrob(
    d = data,
    theme = ttheme
  )
  gt$widths <- ((w_unit / sum(w_unit)) * width)
  gt$heights <- rep(grid::unit(as.numeric(height) / 4, grid::unitType(height)), nrow(gt))

  vp <- grid::viewport(
    x = grid::unit(x, "npc") + grid::unit(1, "lines"),
    y = grid::unit(y, "npc") + grid::unit(1.5, "lines"),
    height = height,
    width = width,
    just = c("right", "top")
  )

  grid::gList(
    grid::gTree(
      vp = vp,
      children = grid::gList(gt)
    )
  )
}

#' Helper function to create grid object with y-axis annotation
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Build the y-axis annotation from a decomposed `ggplot`.
#'
#' @param ylab (`gtable`)\cr the y-lab as a graphical object derived from a `ggplot`.
#' @param yaxis (`gtable`)\cr the y-axis as a graphical object derived from a `ggplot`.
#'
#' @return A `gTree` object containing the y-axis annotation from a `ggplot`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' fit_km <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   survfit(formula = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
#' data_plot <- h_data_plot(fit_km = fit_km)
#' xticks <- h_xticks(data = data_plot)
#' gg <- h_ggkm(
#'   data = data_plot,
#'   censor_show = TRUE,
#'   xticks = xticks, xlab = "Days", ylab = "Survival Probability",
#'   title = "title", footnotes = "footnotes", yval = "Survival"
#' )
#'
#' g_el <- h_decompose_gg(gg)
#'
#' grid::grid.newpage()
#' pvp <- grid::plotViewport(margins = c(5, 4, 2, 20))
#' pushViewport(pvp)
#' grid::grid.draw(h_grob_y_annot(ylab = g_el$ylab, yaxis = g_el$yaxis))
#' grid.rect(gp = grid::gpar(lty = 1, col = "gray35", fill = NA))
#' }
#'
#' @export
h_grob_y_annot <- function(ylab, yaxis) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_grob_y_annot()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  grid::gList(
    grid::gTree(
      vp = grid::viewport(
        width = grid::convertX(yaxis$widths + ylab$widths, "pt"),
        x = grid::unit(1, "npc"),
        just = "right"
      ),
      children = grid::gList(cbind(ylab, yaxis))
    )
  )
}

#' Helper function to create Cox-PH grobs
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Grob of `rtable` output from [h_tbl_coxph_pairwise()]
#'
#' @inheritParams h_grob_median_surv
#' @param ... arguments to pass to [h_tbl_coxph_pairwise()].
#' @param x (`proportion`)\cr a value between 0 and 1 specifying x-location.
#' @param y (`proportion`)\cr a value between 0 and 1 specifying y-location.
#' @param width (`grid::unit`)\cr width (as a unit) to use when printing the grob.
#'
#' @return A `grob` of a table containing statistics `HR`, `XX% CI` (`XX` taken from `control_coxph_pw`),
#'   and `p-value (log-rank)`.
#'
#' @examples
#' \donttest{
#' library(dplyr)
#' library(survival)
#' library(grid)
#'
#' grid::grid.newpage()
#' grid.rect(gp = grid::gpar(lty = 1, col = "pink", fill = "gray85", lwd = 1))
#' data <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#' tbl_grob <- h_grob_coxph(
#'   df = data,
#'   variables = list(tte = "AVAL", is_event = "is_event", arm = "ARMCD"),
#'   control_coxph_pw = control_coxph(conf_level = 0.9), x = 0.5, y = 0.5
#' )
#' grid::grid.draw(tbl_grob)
#' }
#'
#' @export
h_grob_coxph <- function(...,
                         x = 0,
                         y = 0,
                         width = grid::unit(0.4, "npc"),
                         ttheme = gridExtra::ttheme_default(
                           padding = grid::unit(c(1, .5), "lines"),
                           core = list(bg_params = list(fill = c("grey95", "grey90"), alpha = .5))
                         )) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "h_grob_coxph()",
    details = "`g_km` now generates `ggplot` objects. This function is no longer used within `tern`."
  )
  data <- h_tbl_coxph_pairwise(...)

  width <- grid::convertUnit(grid::unit(as.numeric(width), grid::unitType(width)), "in")
  height <- width * (nrow(data) + 1) / 12

  w <- paste("    ", c(
    rownames(data)[which.max(nchar(rownames(data)))],
    sapply(names(data), function(x) c(x, data[[x]])[which.max(nchar(c(x, data[[x]])))])
  ))
  w_unit <- grid::convertWidth(grid::stringWidth(w), "in", valueOnly = TRUE)

  w_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strwidth(w[4], units = "in")
  })
  f_size_w <- which.max(w_txt[w_txt < as.numeric((w_unit / sum(w_unit)) * width)[4]])

  h_txt <- sapply(1:64, function(x) {
    graphics::par(ps = x)
    graphics::strheight(grid::stringHeight("X"), units = "in")
  })
  f_size_h <- which.max(h_txt[h_txt < as.numeric(grid::unit(as.numeric(height) / 4, grid::unitType(height)))])

  if (ttheme$core$fg_params$fontsize == 12) {
    ttheme$core$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$colhead$fg_params$fontsize <- min(f_size_w, f_size_h)
    ttheme$rowhead$fg_params$fontsize <- min(f_size_w, f_size_h)
  }

  tryCatch(
    expr = {
      gt <- gridExtra::tableGrob(
        d = data,
        theme = ttheme
      ) # ERROR 'data' must be of a vector type, was 'NULL'
      gt$widths <- ((w_unit / sum(w_unit)) * width)
      gt$heights <- rep(grid::unit(as.numeric(height) / 4, grid::unitType(height)), nrow(gt))
      vp <- grid::viewport(
        x = grid::unit(x, "npc") + grid::unit(1, "lines"),
        y = grid::unit(y, "npc") + grid::unit(1.5, "lines"),
        height = height,
        width = width,
        just = c("left", "bottom")
      )
      grid::gList(
        grid::gTree(
          vp = vp,
          children = grid::gList(gt)
        )
      )
    },
    error = function(w) {
      message(paste(
        "Warning: Cox table will not be displayed as there is",
        "not any level to be compared in the arm variable."
      ))
      return(
        grid::gList(
          grid::gTree(
            vp = NULL,
            children = NULL
          )
        )
      )
    }
  )
}

#' Convert `rtable` objects to `ggplot` objects
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Given a [rtables::rtable()] object, performs basic conversion to a [ggplot2::ggplot()] object built using
#' functions from the `ggplot2` package. Any table titles and/or footnotes are ignored.
#'
#' @param tbl (`VTableTree`)\cr `rtables` table object.
#' @param fontsize (`numeric(1)`)\cr font size.
#' @param colwidths (`numeric` or `NULL`)\cr a vector of column widths. Each element's position in
#'   `colwidths` corresponds to the column of `tbl` in the same position. If `NULL`, column widths
#'   are calculated according to maximum number of characters per column.
#' @param lbl_col_padding (`numeric`)\cr additional padding to use when calculating spacing between
#'   the first (label) column and the second column of `tbl`. If `colwidths` is specified,
#'   the width of the first column becomes `colwidths[1] + lbl_col_padding`. Defaults to 0.
#'
#' @return A `ggplot` object.
#'
#' @examples
#' dta <- data.frame(
#'   ARM     = rep(LETTERS[1:3], rep(6, 3)),
#'   AVISIT  = rep(paste0("V", 1:3), 6),
#'   AVAL    = c(9:1, rep(NA, 9))
#' )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(vars = "AVAL")
#'
#' tbl <- build_table(lyt, df = dta)
#'
#' rtable2gg(tbl)
#'
#' rtable2gg(tbl, fontsize = 15, colwidths = c(2, 1, 1, 1))
#'
#' @export
rtable2gg <- function(tbl, fontsize = 12, colwidths = NULL, lbl_col_padding = 0) {
  mat <- rtables::matrix_form(tbl, indent_rownames = TRUE)
  mat_strings <- formatters::mf_strings(mat)
  mat_aligns <- formatters::mf_aligns(mat)
  mat_indent <- formatters::mf_rinfo(mat)$indent
  mat_display <- formatters::mf_display(mat)
  nlines_hdr <- formatters::mf_nlheader(mat)
  shared_hdr_rows <- which(apply(mat_display, 1, function(x) (any(!x))))

  tbl_df <- data.frame(mat_strings)
  body_rows <- seq(nlines_hdr + 1, nrow(tbl_df))
  mat_aligns <- apply(mat_aligns, 1:2, function(x) if (x == "left") 0 else if (x == "right") 1 else 0.5)

  # Apply indentation in first column
  tbl_df[body_rows, 1] <- sapply(body_rows, function(i) {
    ind_i <- mat_indent[i - nlines_hdr] * 4
    if (ind_i > 0) paste0(paste(rep(" ", ind_i), collapse = ""), tbl_df[i, 1]) else tbl_df[i, 1]
  })

  # Get column widths
  if (is.null(colwidths)) {
    colwidths <- apply(tbl_df, 2, function(x) max(nchar(x))) + 1
  }
  tot_width <- sum(colwidths) + lbl_col_padding

  if (length(shared_hdr_rows) > 0) {
    tbl_df <- tbl_df[-shared_hdr_rows, ]
    mat_aligns <- mat_aligns[-shared_hdr_rows, ]
  }

  res <- ggplot(data = tbl_df) +
    theme_void() +
    scale_x_continuous(limits = c(0, tot_width)) +
    scale_y_continuous(limits = c(0, nrow(mat_strings))) +
    annotate(
      "segment",
      x = 0, xend = tot_width,
      y = nrow(mat_strings) - nlines_hdr + 0.5, yend = nrow(mat_strings) - nlines_hdr + 0.5
    )

  # If header content spans multiple columns, center over these columns
  if (length(shared_hdr_rows) > 0) {
    mat_strings[shared_hdr_rows, ] <- trimws(mat_strings[shared_hdr_rows, ])
    for (hr in shared_hdr_rows) {
      hdr_lbls <- mat_strings[1:hr, mat_display[hr, -1]]
      hdr_lbls <- matrix(hdr_lbls[nzchar(hdr_lbls)], nrow = hr)
      for (idx_hl in seq_len(ncol(hdr_lbls))) {
        cur_lbl <- tail(hdr_lbls[, idx_hl], 1)
        which_cols <- if (hr == 1) {
          which(mat_strings[hr, ] == hdr_lbls[idx_hl])
        } else { # for >2 col splits, only print labels for each unique combo of nested columns
          which(
            apply(mat_strings[1:hr, ], 2, function(x) all(x == hdr_lbls[1:hr, idx_hl]))
          )
        }
        line_pos <- c(
          sum(colwidths[1:(which_cols[1] - 1)]) + 1 + lbl_col_padding,
          sum(colwidths[1:max(which_cols)]) - 1 + lbl_col_padding
        )

        res <- res +
          annotate(
            "text",
            x = mean(line_pos),
            y = nrow(mat_strings) + 1 - hr,
            label = cur_lbl,
            size = fontsize / .pt
          ) +
          annotate(
            "segment",
            x = line_pos[1],
            xend = line_pos[2],
            y = nrow(mat_strings) - hr + 0.5,
            yend = nrow(mat_strings) - hr + 0.5
          )
      }
    }
  }

  # Add table columns
  for (i in seq_len(ncol(tbl_df))) {
    res <- res + annotate(
      "text",
      x = if (i == 1) 0 else sum(colwidths[1:i]) - 0.5 * colwidths[i] + lbl_col_padding,
      y = rev(seq_len(nrow(tbl_df))),
      label = tbl_df[, i],
      hjust = mat_aligns[, i],
      size = fontsize / .pt
    )
  }

  res
}

#' Convert `data.frame` object to `ggplot` object
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Given a `data.frame` object, performs basic conversion to a [ggplot2::ggplot()] object built using
#' functions from the `ggplot2` package.
#'
#' @param df (`data.frame`)\cr a data frame.
#' @param colwidths (`numeric` or `NULL`)\cr a vector of column widths. Each element's position in
#'   `colwidths` corresponds to the column of `df` in the same position. If `NULL`, column widths
#'   are calculated according to maximum number of characters per column.
#' @param font_size (`numeric(1)`)\cr font size.
#' @param col_labels (`flag`)\cr whether the column names (labels) of `df` should be used as the first row
#'   of the output table.
#' @param col_lab_fontface (`string`)\cr font face to apply to the first row (of column labels
#'   if `col_labels = TRUE`). Defaults to `"bold"`.
#' @param hline (`flag`)\cr whether a horizontal line should be printed below the first row of the table.
#' @param bg_fill (`string`)\cr table background fill color.
#'
#' @return A `ggplot` object.
#'
#' @examples
#' \dontrun{
#' df2gg(head(iris, 5))
#'
#' df2gg(head(iris, 5), font_size = 15, colwidths = c(1, 1, 1, 1, 1))
#' }
#' @keywords internal
df2gg <- function(df,
                  colwidths = NULL,
                  font_size = 10,
                  col_labels = TRUE,
                  col_lab_fontface = "bold",
                  hline = TRUE,
                  bg_fill = NULL) {
  # convert to text
  df <- as.data.frame(apply(df, 1:2, function(x) if (is.na(x)) "NA" else as.character(x)))

  if (col_labels) {
    df <- as.matrix(df)
    df <- rbind(colnames(df), df)
  }

  # Get column widths
  if (is.null(colwidths)) {
    colwidths <- apply(df, 2, function(x) max(nchar(x), na.rm = TRUE))
  }
  tot_width <- sum(colwidths)

  res <- ggplot(data = df) +
    theme_void() +
    scale_x_continuous(limits = c(0, tot_width)) +
    scale_y_continuous(limits = c(1, nrow(df)))

  if (!is.null(bg_fill)) res <- res + theme(plot.background = element_rect(fill = bg_fill))

  if (hline) {
    res <- res +
      annotate(
        "segment",
        x = 0 + 0.2 * colwidths[2], xend = tot_width - 0.1 * tail(colwidths, 1),
        y = nrow(df) - 0.5, yend = nrow(df) - 0.5
      )
  }

  for (i in seq_len(ncol(df))) {
    line_pos <- c(
      if (i == 1) 0 else sum(colwidths[1:(i - 1)]),
      sum(colwidths[1:i])
    )
    res <- res +
      annotate(
        "text",
        x = mean(line_pos),
        y = rev(seq_len(nrow(df))),
        label = df[, i],
        size = font_size / .pt,
        fontface = if (col_labels) {
          c(col_lab_fontface, rep("plain", nrow(df) - 1))
        } else {
          rep("plain", nrow(df))
        }
      )
  }

  res
}

#' Cumulative counts of numeric variable by thresholds
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_cumulative()] creates a layout element to calculate cumulative counts of values in a
#' numeric variable that are less than, less or equal to, greater than, or greater or equal to user-specified
#' threshold values.
#'
#' This function analyzes numeric variable `vars` against the threshold values supplied to the `thresholds`
#' argument as a numeric vector. Whether counts should include the threshold values, and whether to count
#' values lower or higher than the threshold values can be set via the `include_eq` and `lower_tail`
#' parameters, respectively.
#'
#' @inheritParams h_count_cumulative
#' @inheritParams argument_convention
#' @param thresholds (`numeric`)\cr vector of cutoff values for the counts.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_cumulative"), type = "sh")``
#'
#' @seealso Relevant helper function [h_count_cumulative()], and descriptive function [d_count_cumulative()].
#'
#' @name count_cumulative
#' @order 1
NULL

#' Helper function for `s_count_cumulative()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to calculate count and fraction of `x` values in the lower or upper tail given a threshold.
#'
#' @inheritParams argument_convention
#' @param threshold (`numeric(1)`)\cr a cutoff value as threshold to count values of `x`.
#' @param lower_tail (`flag`)\cr whether to count lower tail, default is `TRUE`.
#' @param include_eq (`flag`)\cr whether to include value equal to the `threshold` in
#'   count, default is `TRUE`.
#'
#' @return A named vector with items:
#'   * `count`: the count of values less than, less or equal to, greater than, or greater or equal to a threshold
#'     of user specification.
#'   * `fraction`: the fraction of the count.
#'
#' @seealso [count_cumulative]
#'
#' @examples
#' set.seed(1, kind = "Mersenne-Twister")
#' x <- c(sample(1:10, 10), NA)
#' .N_col <- length(x)
#'
#' h_count_cumulative(x, 5, .N_col = .N_col)
#' h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col)
#' h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col)
#' h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col)
#'
#' @export
h_count_cumulative <- function(x,
                               threshold,
                               lower_tail = TRUE,
                               include_eq = TRUE,
                               na.rm = TRUE, # nolint
                               .N_col) { # nolint
  checkmate::assert_numeric(x)
  checkmate::assert_numeric(threshold)
  checkmate::assert_numeric(.N_col)
  checkmate::assert_flag(lower_tail)
  checkmate::assert_flag(include_eq)
  checkmate::assert_flag(na.rm)

  is_keep <- if (na.rm) !is.na(x) else rep(TRUE, length(x))
  count <- if (lower_tail && include_eq) {
    length(x[is_keep & x <= threshold])
  } else if (lower_tail && !include_eq) {
    length(x[is_keep & x < threshold])
  } else if (!lower_tail && include_eq) {
    length(x[is_keep & x >= threshold])
  } else if (!lower_tail && !include_eq) {
    length(x[is_keep & x > threshold])
  }

  result <- c(
    count = count,
    fraction = if (count == 0 && .N_col == 0) 0 else count / .N_col
  )
  result
}

#' Description of cumulative count
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a helper function that describes the analysis in [s_count_cumulative()].
#'
#' @inheritParams h_count_cumulative
#'
#' @return Labels for [s_count_cumulative()].
#'
#' @export
d_count_cumulative <- function(threshold, lower_tail = TRUE, include_eq = TRUE) {
  checkmate::assert_numeric(threshold)
  lg <- if (lower_tail) "<" else ">"
  eq <- if (include_eq) "=" else ""
  paste0(lg, eq, " ", threshold)
}

#' @describeIn count_cumulative Statistics function that produces a named list given a numeric vector of thresholds.
#'
#' @return
#' * `s_count_cumulative()` returns a named list of `count_fraction`s: a list with each `thresholds` value as a
#'   component, each component containing a vector for the count and fraction.
#'
#' @keywords internal
s_count_cumulative <- function(x,
                               thresholds,
                               lower_tail = TRUE,
                               include_eq = TRUE,
                               .N_col, # nolint
                               .N_row, # nolint
                               denom = c("N_col", "n", "N_row"),
                               ...) {
  checkmate::assert_numeric(thresholds, min.len = 1, any.missing = FALSE)

  denom <- match.arg(denom) %>%
    switch(
      n = length(x),
      N_row = .N_row,
      N_col = .N_col
    )

  count_fraction_list <- Map(function(thres) {
    result <- h_count_cumulative(x, thres, lower_tail, include_eq, .N_col = denom, ...)
    label <- d_count_cumulative(thres, lower_tail, include_eq)
    formatters::with_label(result, label)
  }, thresholds)

  names(count_fraction_list) <- thresholds
  list(count_fraction = count_fraction_list)
}

#' @describeIn count_cumulative Formatted analysis function which is used as `afun`
#'   in `count_cumulative()`.
#'
#' @return
#' * `a_count_cumulative()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_cumulative <- make_afun(
  s_count_cumulative,
  .formats = c(count_fraction = format_count_fraction)
)

#' @describeIn count_cumulative Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_cumulative()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_cumulative()` to the table layout.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_cumulative(
#'     vars = "AGE",
#'     thresholds = c(40, 60)
#'   ) %>%
#'   build_table(tern_ex_adsl)
#'
#' @export
#' @order 2
count_cumulative <- function(lyt,
                             vars,
                             thresholds,
                             lower_tail = TRUE,
                             include_eq = TRUE,
                             var_labels = vars,
                             show_labels = "visible",
                             na_str = default_na_str(),
                             nested = TRUE,
                             ...,
                             table_names = vars,
                             .stats = NULL,
                             .formats = NULL,
                             .labels = NULL,
                             .indent_mods = NULL) {
  extra_args <- list(thresholds = thresholds, lower_tail = lower_tail, include_eq = include_eq, ...)

  afun <- make_afun(
    a_count_cumulative,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    table_names = table_names,
    var_labels = var_labels,
    show_labels = show_labels,
    nested = nested,
    extra_args = extra_args
  )
}

#' Line plot with optional table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Line plot with optional table.
#'
#' @inheritParams argument_convention
#' @param alt_counts_df (`data.frame` or `NULL`)\cr data set that will be used (only)
#'   to counts objects in groups for stratification.
#' @param variables (named `character`) vector of variable names in `df` which should include:
#'   * `x` (`string`)\cr name of x-axis variable.
#'   * `y` (`string`)\cr name of y-axis variable.
#'   * `group_var` (`string` or `NULL`)\cr name of grouping variable (or strata), i.e. treatment arm.
#'     Can be `NA` to indicate lack of groups.
#'   * `subject_var` (`string` or `NULL`)\cr name of subject variable. Only applies if `group_var` is
#'      not NULL.
#'   * `paramcd` (`string` or `NA`)\cr name of the variable for parameter's code. Used for y-axis label and plot's
#'     subtitle. Can be `NA` if `paramcd` is not to be added to the y-axis label or subtitle.
#'   * `y_unit` (`string` or `NA`)\cr name of variable with units of `y`. Used for y-axis label and plot's subtitle.
#'     Can be `NA` if y unit is not to be added to the y-axis label or subtitle.
#'   * `facet_var` (`string` or `NA`)\cr name of the secondary grouping variable used for plot faceting, i.e. treatment
#'     arm. Can be `NA` to indicate lack of groups.
#' @param mid (`character` or `NULL`)\cr names of the statistics that will be plotted as midpoints.
#'   All the statistics indicated in `mid` variable must be present in the object returned by `sfun`,
#'   and be of a `double` or `numeric` type vector of length one.
#' @param interval (`character` or `NULL`)\cr names of the statistics that will be plotted as intervals.
#'   All the statistics indicated in `interval` variable must be present in the object returned by `sfun`,
#'   and be of a `double` or `numeric` type vector of length two. Set `interval = NULL` if intervals should not be
#'   added to the plot.
#' @param whiskers (`character`)\cr names of the interval whiskers that will be plotted. Names must match names
#'   of the list element `interval` that will be returned by `sfun` (e.g. `mean_ci_lwr` element of
#'   `sfun(x)[["mean_ci"]]`). It is possible to specify one whisker only, or to suppress all whiskers by setting
#'   `interval = NULL`.
#' @param table (`character` or `NULL`)\cr names of the statistics that will be displayed in the table below the plot.
#'   All the statistics indicated in `table` variable must be present in the object returned by `sfun`.
#' @param sfun (`function`)\cr the function to compute the values of required statistics. It must return a named `list`
#'   with atomic vectors. The names of the `list` elements refer to the names of the statistics and are used by `mid`,
#'   `interval`, `table`. It must be able to accept as input a vector with data for which statistics are computed.
#' @param ... optional arguments to `sfun`.
#' @param mid_type (`string`)\cr controls the type of the `mid` plot, it can be point (`"p"`), line (`"l"`),
#'   or point and line (`"pl"`).
#' @param mid_point_size (`numeric(1)`)\cr font size of the `mid` plot points.
#' @param position (`character` or `call`)\cr geom element position adjustment, either as a string, or the result of
#'   a call to a position adjustment function.
#' @param legend_title (`string`)\cr legend title.
#' @param legend_position (`string`)\cr the position of the plot legend (`"none"`, `"left"`, `"right"`, `"bottom"`,
#'   `"top"`, or a two-element numeric vector).
#' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to control styling of the plot.
#' @param xticks (`numeric` or `NULL`)\cr numeric vector of tick positions or a single number with spacing
#'   between ticks on the x-axis, for use when `variables$x` is numeric. If `NULL` (default), [labeling::extended()] is
#'   used to determine optimal tick positions on the x-axis. If `variables$x` is not numeric, this argument is ignored.
#' @param x_lab (`string` or `NULL`)\cr x-axis label. If `NULL` then no label will be added.
#' @param y_lab (`string` or `NULL`)\cr y-axis label. If `NULL` then no label will be added.
#' @param y_lab_add_paramcd (`flag`)\cr whether `paramcd`, i.e. `unique(df[[variables["paramcd"]]])` should be added
#'   to the y-axis label (`y_lab`).
#' @param y_lab_add_unit (`flag`)\cr whether y-axis unit, i.e. `unique(df[[variables["y_unit"]]])` should be added
#'   to the y-axis label (`y_lab`).
#' @param title (`string`)\cr plot title.
#' @param subtitle (`string`)\cr plot subtitle.
#' @param subtitle_add_paramcd (`flag`)\cr whether `paramcd`, i.e. `unique(df[[variables["paramcd"]]])` should be
#'   added to the plot's subtitle (`subtitle`).
#' @param subtitle_add_unit (`flag`)\cr whether the y-axis unit, i.e. `unique(df[[variables["y_unit"]]])` should be
#'   added to the plot's subtitle (`subtitle`).
#' @param caption (`string`)\cr optional caption below the plot.
#' @param table_format (named `character` or `NULL`)\cr format patterns for descriptive statistics used in the
#'   (optional) table appended to the plot. It is passed directly to the `h_format_row` function through the `format`
#'   parameter. Names of `table_format` must match the names of statistics returned by `sfun` function.
#' @param table_labels (named `character` or `NULL`)\cr labels for descriptive statistics used in the (optional) table
#'   appended to the plot. Names of `table_labels` must match the names of statistics returned by `sfun` function.
#' @param table_font_size (`numeric(1)`)\cr font size of the text in the table.
#' @param newpage `r lifecycle::badge("deprecated")` not used.
#' @param col (`character`)\cr color(s). See `?ggplot2::aes_colour_fill_alpha` for example values.
#' @param linetype (`character`)\cr line type(s). See `?ggplot2::aes_linetype_size_shape` for example values.
#' @param errorbar_width (`numeric(1)`)\cr width of the error bars.
#' @param rel_height_plot (`proportion`)\cr proportion of total figure height to allocate to the line plot.
#'   Relative height of annotation table is then `1 - rel_height_plot`. If `table = NULL`, this parameter is ignored.
#' @param as_list (`flag`)\cr whether the two `ggplot` objects should be returned as a list when `table` is not `NULL`.
#'   If `TRUE`, a named list with two elements, `plot` and `table`, will be returned. If `FALSE` (default) the
#'   annotation table is printed below the plot via [cowplot::plot_grid()].
#'
#' @return A `ggplot` line plot (and statistics table if applicable).
#'
#' @examples
#'
#' adsl <- tern_ex_adsl
#' adlb <- tern_ex_adlb %>% dplyr::filter(ANL01FL == "Y", PARAMCD == "ALT", AVISIT != "SCREENING")
#' adlb$AVISIT <- droplevels(adlb$AVISIT)
#' adlb <- dplyr::mutate(adlb, AVISIT = forcats::fct_reorder(AVISIT, AVISITN, min))
#'
#' # Mean with CI
#' g_lineplot(adlb, adsl, subtitle = "Laboratory Test:")
#'
#' # Mean with CI, no stratification with group_var
#' g_lineplot(adlb, variables = control_lineplot_vars(group_var = NA))
#'
#' # Mean, upper whisker of CI, no group_var(strata) counts N
#' g_lineplot(
#'   adlb,
#'   whiskers = "mean_ci_upr",
#'   title = "Plot of Mean and Upper 95% Confidence Limit by Visit"
#' )
#'
#' # Median with CI
#' g_lineplot(
#'   adlb,
#'   adsl,
#'   mid = "median",
#'   interval = "median_ci",
#'   whiskers = c("median_ci_lwr", "median_ci_upr"),
#'   title = "Plot of Median and 95% Confidence Limits by Visit"
#' )
#'
#' # Mean, +/- SD
#' g_lineplot(adlb, adsl,
#'   interval = "mean_sdi",
#'   whiskers = c("mean_sdi_lwr", "mean_sdi_upr"),
#'   title = "Plot of Median +/- SD by Visit"
#' )
#'
#' # Mean with CI plot with stats table
#' g_lineplot(adlb, adsl, table = c("n", "mean", "mean_ci"))
#'
#' # Mean with CI, table and customized confidence level
#' g_lineplot(
#'   adlb,
#'   adsl,
#'   table = c("n", "mean", "mean_ci"),
#'   control = control_analyze_vars(conf_level = 0.80),
#'   title = "Plot of Mean and 80% Confidence Limits by Visit"
#' )
#'
#' # Mean with CI, table, filtered data
#' adlb_f <- dplyr::filter(adlb, ARMCD != "ARM A" | AVISIT == "BASELINE")
#' g_lineplot(adlb_f, table = c("n", "mean"))
#'
#' @export
g_lineplot <- function(df,
                       alt_counts_df = NULL,
                       variables = control_lineplot_vars(),
                       mid = "mean",
                       interval = "mean_ci",
                       whiskers = c("mean_ci_lwr", "mean_ci_upr"),
                       table = NULL,
                       sfun = s_summary,
                       ...,
                       mid_type = "pl",
                       mid_point_size = 2,
                       position = ggplot2::position_dodge(width = 0.4),
                       legend_title = NULL,
                       legend_position = "bottom",
                       ggtheme = nestcolor::theme_nest(),
                       xticks = NULL,
                       xlim = NULL,
                       ylim = NULL,
                       x_lab = obj_label(df[[variables[["x"]]]]),
                       y_lab = NULL,
                       y_lab_add_paramcd = TRUE,
                       y_lab_add_unit = TRUE,
                       title = "Plot of Mean and 95% Confidence Limits by Visit",
                       subtitle = "",
                       subtitle_add_paramcd = TRUE,
                       subtitle_add_unit = TRUE,
                       caption = NULL,
                       table_format = NULL,
                       table_labels = NULL,
                       table_font_size = 3,
                       errorbar_width = 0.45,
                       newpage = lifecycle::deprecated(),
                       col = NULL,
                       linetype = NULL,
                       rel_height_plot = 0.5,
                       as_list = FALSE) {
  checkmate::assert_character(variables, any.missing = TRUE)
  checkmate::assert_character(mid, null.ok = TRUE)
  checkmate::assert_character(interval, null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)
  checkmate::assert_character(linetype, null.ok = TRUE)
  checkmate::assert_numeric(xticks, null.ok = TRUE)
  checkmate::assert_numeric(xlim, finite = TRUE, any.missing = FALSE, len = 2, sorted = TRUE, null.ok = TRUE)
  checkmate::assert_numeric(ylim, finite = TRUE, any.missing = FALSE, len = 2, sorted = TRUE, null.ok = TRUE)
  checkmate::assert_number(errorbar_width, lower = 0)
  checkmate::assert_string(title, null.ok = TRUE)
  checkmate::assert_string(subtitle, null.ok = TRUE)
  assert_proportion_value(rel_height_plot)
  checkmate::assert_logical(as_list)

  if (!is.null(table)) {
    table_format <- get_formats_from_stats(table)
    table_labels <- get_labels_from_stats(table) %>% .unlist_keep_nulls()
  }

  extra_args <- list(...)
  if ("control" %in% names(extra_args)) {
    if (!is.null(table) && all(table_labels == .unlist_keep_nulls(get_labels_from_stats(table)))) {
      table_labels <- table_labels %>% labels_use_control(extra_args[["control"]])
    }
  }

  if (is.character(interval)) {
    checkmate::assert_vector(whiskers, min.len = 0, max.len = 2)
  }

  if (length(whiskers) == 1) {
    checkmate::assert_character(mid)
  }

  if (is.character(mid)) {
    checkmate::assert_scalar(mid_type)
    checkmate::assert_subset(mid_type, c("pl", "p", "l"))
  }

  x <- variables[["x"]]
  y <- variables[["y"]]
  paramcd <- variables["paramcd"] # NA if paramcd == NA or it is not in variables
  y_unit <- variables["y_unit"] # NA if y_unit == NA or it is not in variables
  if (is.na(variables["group_var"])) {
    group_var <- NULL # NULL if group_var == NA or it is not in variables
  } else {
    group_var <- variables[["group_var"]]
    subject_var <- variables[["subject_var"]]
  }
  if (is.na(variables["facet_var"])) {
    facet_var <- NULL # NULL if facet_var == NA or it is not in variables
  } else {
    facet_var <- variables[["facet_var"]]
  }
  checkmate::assert_flag(y_lab_add_paramcd, null.ok = TRUE)
  checkmate::assert_flag(subtitle_add_paramcd, null.ok = TRUE)
  if ((!is.null(y_lab) && y_lab_add_paramcd) || (!is.null(subtitle) && subtitle_add_paramcd)) {
    checkmate::assert_false(is.na(paramcd))
    checkmate::assert_scalar(unique(df[[paramcd]]))
  }

  checkmate::assert_flag(y_lab_add_unit, null.ok = TRUE)
  checkmate::assert_flag(subtitle_add_unit, null.ok = TRUE)
  if ((!is.null(y_lab) && y_lab_add_unit) || (!is.null(subtitle) && subtitle_add_unit)) {
    checkmate::assert_false(is.na(y_unit))
    checkmate::assert_scalar(unique(df[[y_unit]]))
  }

  if (!is.null(group_var) && !is.null(alt_counts_df)) {
    checkmate::assert_set_equal(unique(alt_counts_df[[group_var]]), unique(df[[group_var]]))
  }

  ####################################### |
  # ---- Compute required statistics ----
  ####################################### |
  # Remove unused levels for x-axis
  if (is.factor(df[[x]])) {
    df[[x]] <- droplevels(df[[x]])
  }

  if (!is.null(facet_var) && !is.null(group_var)) {
    df_grp <- tidyr::expand(df, .data[[facet_var]], .data[[group_var]], .data[[x]]) # expand based on levels of factors
  } else if (!is.null(group_var)) {
    df_grp <- tidyr::expand(df, .data[[group_var]], .data[[x]]) # expand based on levels of factors
  } else {
    df_grp <- tidyr::expand(df, NULL, .data[[x]])
  }

  df_grp <- df_grp %>%
    dplyr::full_join(y = df[, c(facet_var, group_var, x, y)], by = c(facet_var, group_var, x), multiple = "all") %>%
    dplyr::group_by_at(c(facet_var, group_var, x))

  df_stats <- df_grp %>%
    dplyr::summarise(
      data.frame(t(do.call(c, unname(sfun(.data[[y]])[c(mid, interval)])))),
      .groups = "drop"
    )

  df_stats <- df_stats[!is.na(df_stats[[mid]]), ]

  # add number of objects N in group_var (strata)
  if (!is.null(group_var) && !is.null(alt_counts_df)) {
    strata_N <- paste0(group_var, "_N") # nolint

    df_N <- stats::aggregate(eval(parse(text = subject_var)) ~ eval(parse(text = group_var)), data = alt_counts_df, FUN = function(x) length(unique(x))) # nolint
    colnames(df_N) <- c(group_var, "N") # nolint
    df_N[[strata_N]] <- paste0(df_N[[group_var]], " (N = ", df_N$N, ")") # nolint

    # keep strata factor levels
    matches <- sapply(unique(df_N[[group_var]]), function(x) {
      regex_pattern <- gsub("([][(){}^$.|*+?\\\\])", "\\\\\\1", x)
      unique(df_N[[paste0(group_var, "_N")]])[grepl(
        paste0("^", regex_pattern),
        unique(df_N[[paste0(group_var, "_N")]])
      )]
    })
    df_N[[paste0(group_var, "_N")]] <- factor(df_N[[group_var]]) # nolint
    levels(df_N[[paste0(group_var, "_N")]]) <- unlist(matches) # nolint

    # strata_N should not be in colnames(df_stats)
    checkmate::assert_disjunct(strata_N, colnames(df_stats))

    df_stats <- merge(x = df_stats, y = df_N[, c(group_var, strata_N)], by = group_var)
  } else if (!is.null(group_var)) {
    strata_N <- group_var # nolint
  } else {
    strata_N <- NULL # nolint
  }

  ############################################### |
  # ---- Prepare certain plot's properties. ----
  ############################################### |
  # legend title
  if (is.null(legend_title) && !is.null(group_var) && legend_position != "none") {
    legend_title <- attr(df[[group_var]], "label")
  }

  # y label
  if (!is.null(y_lab)) {
    if (y_lab_add_paramcd) {
      y_lab <- paste(y_lab, unique(df[[paramcd]]))
    }

    if (y_lab_add_unit) {
      y_lab <- paste0(y_lab, " (", unique(df[[y_unit]]), ")")
    }

    y_lab <- trimws(y_lab)
  }

  # subtitle
  if (!is.null(subtitle)) {
    if (subtitle_add_paramcd) {
      subtitle <- paste(subtitle, unique(df[[paramcd]]))
    }

    if (subtitle_add_unit) {
      subtitle <- paste0(subtitle, " (", unique(df[[y_unit]]), ")")
    }

    subtitle <- trimws(subtitle)
  }

  ############################### |
  # ---- Build plot object. ----
  ############################### |
  p <- ggplot2::ggplot(
    data = df_stats,
    mapping = ggplot2::aes(
      x = .data[[x]], y = .data[[mid]],
      color = if (is.null(strata_N)) NULL else .data[[strata_N]],
      shape = if (is.null(strata_N)) NULL else .data[[strata_N]],
      lty = if (is.null(strata_N)) NULL else .data[[strata_N]],
      group = if (is.null(strata_N)) NULL else .data[[strata_N]]
    )
  )

  if (!is.null(group_var) && nlevels(df_stats[[strata_N]]) > 6) {
    p <- p +
      scale_shape_manual(values = seq(15, 15 + nlevels(df_stats[[strata_N]])))
  }

  if (!is.null(mid)) {
    # points
    if (grepl("p", mid_type, fixed = TRUE)) {
      p <- p + ggplot2::geom_point(position = position, size = mid_point_size, na.rm = TRUE)
    }

    # lines - plotted only if there is a strata grouping (group_var)
    if (grepl("l", mid_type, fixed = TRUE) && !is.null(strata_N)) { # nolint
      p <- p + ggplot2::geom_line(position = position, na.rm = TRUE)
    }
  }

  # interval
  if (!is.null(interval)) {
    p <- p +
      ggplot2::geom_errorbar(
        ggplot2::aes(ymin = .data[[whiskers[1]]], ymax = .data[[whiskers[max(1, length(whiskers))]]]),
        width = errorbar_width,
        position = position
      )

    if (length(whiskers) == 1) { # lwr or upr only; mid is then required
      # workaround as geom_errorbar does not provide single-direction whiskers
      p <- p +
        ggplot2::geom_linerange(
          data = df_stats[!is.na(df_stats[[whiskers]]), ], # as na.rm =TRUE does not suppress warnings
          ggplot2::aes(ymin = .data[[mid]], ymax = .data[[whiskers]]),
          position = position,
          na.rm = TRUE,
          show.legend = FALSE
        )
    }
  }

  if (is.numeric(df_stats[[x]])) {
    if (length(xticks) == 1) xticks <- seq(from = min(df_stats[[x]]), to = max(df_stats[[x]]), by = xticks)
    p <- p + ggplot2::scale_x_continuous(breaks = if (!is.null(xticks)) xticks else waiver(), limits = xlim)
  }

  p <- p +
    ggplot2::scale_y_continuous(labels = scales::comma, limits = ylim) +
    ggplot2::labs(
      title = title,
      subtitle = subtitle,
      caption = caption,
      color = legend_title,
      lty = legend_title,
      shape = legend_title,
      x = x_lab,
      y = y_lab
    )

  if (!is.null(col)) {
    p <- p +
      ggplot2::scale_color_manual(values = col)
  }
  if (!is.null(linetype)) {
    p <- p +
      ggplot2::scale_linetype_manual(values = linetype)
  }

  if (!is.null(facet_var)) {
    p <- p +
      facet_grid(cols = vars(df_stats[[facet_var]]))
  }

  if (!is.null(ggtheme)) {
    p <- p + ggtheme
  } else {
    p <- p +
      ggplot2::theme_bw() +
      ggplot2::theme(
        legend.key.width = grid::unit(1, "cm"),
        legend.position = legend_position,
        legend.direction = ifelse(
          legend_position %in% c("top", "bottom"),
          "horizontal",
          "vertical"
        )
      )
  }

  ############################################################# |
  # ---- Optionally, add table to the bottom of the plot. ----
  ############################################################# |
  if (!is.null(table)) {
    df_stats_table <- df_grp %>%
      dplyr::summarise(
        h_format_row(
          x = sfun(.data[[y]], ...)[table],
          format = table_format,
          labels = table_labels
        ),
        .groups = "drop"
      )

    stats_lev <- rev(setdiff(colnames(df_stats_table), c(group_var, x)))

    df_stats_table <- df_stats_table %>%
      tidyr::pivot_longer(
        cols = -dplyr::all_of(c(group_var, x)),
        names_to = "stat",
        values_to = "value",
        names_ptypes = list(stat = factor(levels = stats_lev))
      )

    tbl <- ggplot2::ggplot(
      df_stats_table,
      ggplot2::aes(x = .data[[x]], y = .data[["stat"]], label = .data[["value"]])
    ) +
      ggplot2::geom_text(size = table_font_size) +
      ggplot2::theme_bw() +
      ggplot2::theme(
        panel.border = ggplot2::element_blank(),
        panel.grid.major = ggplot2::element_blank(),
        panel.grid.minor = ggplot2::element_blank(),
        axis.ticks = ggplot2::element_blank(),
        axis.title = ggplot2::element_blank(),
        axis.text.x = ggplot2::element_blank(),
        axis.text.y = ggplot2::element_text(
          size = table_font_size * ggplot2::.pt,
          margin = ggplot2::margin(t = 0, r = 0, b = 0, l = 5)
        ),
        strip.text = ggplot2::element_text(hjust = 0),
        strip.text.x = ggplot2::element_text(
          size = table_font_size * ggplot2::.pt,
          margin = ggplot2::margin(1.5, 0, 1.5, 0, "pt")
        ),
        strip.background = ggplot2::element_rect(fill = "grey95", color = NA),
        legend.position = "none"
      )

    if (!is.null(group_var)) {
      tbl <- tbl + ggplot2::facet_wrap(facets = group_var, ncol = 1)
    }

    if (!as_list) {
      # align plot and table
      cowplot::plot_grid(
        p,
        tbl,
        ncol = 1,
        align = "v",
        axis = "tblr",
        rel_heights = c(rel_height_plot, 1 - rel_height_plot)
      )
    } else {
      list(plot = p, table = tbl)
    }
  } else {
    p
  }
}

#' Helper function to format the optional `g_lineplot` table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param x (named `list`)\cr list of numerical values to be formatted and optionally labeled.
#'   Elements of `x` must be `numeric` vectors.
#' @param format (named `character` or `NULL`)\cr format patterns for `x`. Names of the `format` must
#'   match the names of `x`. This parameter is passed directly to the `rtables::format_rcell`
#'   function through the `format` parameter.
#' @param labels (named `character` or `NULL`)\cr optional labels for `x`. Names of the `labels` must
#'   match the names of `x`. When a label is not specified for an element of `x`,
#'   then this function tries to use `label` or `names` (in this order) attribute of that element
#'   (depending on which one exists and it is not `NULL` or `NA` or `NaN`). If none of these attributes
#'   are attached to a given element of `x`, then the label is automatically generated.
#'
#' @return A single row `data.frame` object.
#'
#' @examples
#' mean_ci <- c(48, 51)
#' x <- list(mean = 50, mean_ci = mean_ci)
#' format <- c(mean = "xx.x", mean_ci = "(xx.xx, xx.xx)")
#' labels <- c(mean = "My Mean")
#' h_format_row(x, format, labels)
#'
#' attr(mean_ci, "label") <- "Mean 95% CI"
#' x <- list(mean = 50, mean_ci = mean_ci)
#' h_format_row(x, format, labels)
#'
#' @export
h_format_row <- function(x, format, labels = NULL) {
  # cell: one row, one column data.frame
  format_cell <- function(x, format, label = NULL) {
    fc <- format_rcell(x = x, format = unlist(format))
    if (is.na(fc)) {
      fc <- "NA"
    }
    x_label <- attr(x, "label")
    if (!is.null(label) && !is.na(label)) {
      names(fc) <- label
    } else if (!is.null(x_label) && !is.na(x_label)) {
      names(fc) <- x_label
    } else if (length(x) == length(fc)) {
      names(fc) <- names(x)
    }
    as.data.frame(t(fc))
  }

  row <- do.call(
    cbind,
    lapply(
      names(x), function(xn) format_cell(x[[xn]], format = format[xn], label = labels[xn])
    )
  )

  row
}

#' Control function for `g_lineplot()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Default values for `variables` parameter in `g_lineplot` function.
#' A variable's default value can be overwritten for any variable.
#'
#' @param x (`string`)\cr x-variable name.
#' @param y (`string`)\cr y-variable name.
#' @param group_var (`string` or `NA`)\cr group variable name.
#' @param subject_var (`string` or `NA`)\cr subject variable name.
#' @param facet_var (`string` or `NA`)\cr faceting variable name.
#' @param paramcd (`string` or `NA`)\cr parameter code variable name.
#' @param y_unit (`string` or `NA`)\cr y-axis unit variable name.
#'
#' @return A named character vector of variable names.
#'
#' @examples
#' control_lineplot_vars()
#' control_lineplot_vars(group_var = NA)
#'
#' @export
control_lineplot_vars <- function(x = "AVISIT",
                                  y = "AVAL",
                                  group_var = "ARM",
                                  facet_var = NA,
                                  paramcd = "PARAMCD",
                                  y_unit = "AVALU",
                                  subject_var = "USUBJID") {
  checkmate::assert_string(x)
  checkmate::assert_string(y)
  checkmate::assert_string(group_var, na.ok = TRUE, null.ok = TRUE)
  checkmate::assert_string(facet_var, na.ok = TRUE, null.ok = TRUE)
  checkmate::assert_string(subject_var, na.ok = TRUE, null.ok = TRUE)
  checkmate::assert_string(paramcd, na.ok = TRUE, null.ok = TRUE)
  checkmate::assert_string(y_unit, na.ok = TRUE, null.ok = TRUE)

  variables <- c(
    x = x, y = y, group_var = group_var, paramcd = paramcd,
    y_unit = y_unit, subject_var = subject_var, facet_var = facet_var
  )
  return(variables)
}

#' Count number of patients and sum exposure across all patients in columns
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [analyze_patients_exposure_in_cols()] creates a layout element to count total numbers of
#' patients and sum an analysis value (i.e. exposure) across all patients in columns.
#'
#' The primary analysis variable `ex_var` is the exposure variable used to calculate the `sum_exposure` statistic. The
#' `id` variable is used to uniquely identify patients in the data such that only unique patients are counted in the
#' `n_patients` statistic, and the `var` variable is used to create a row split if needed. The percentage returned as
#' part of the `n_patients` statistic is the proportion of all records that correspond to a unique patient.
#'
#' The summarize function [summarize_patients_exposure_in_cols()] performs the same function as
#' [analyze_patients_exposure_in_cols()] except it creates content rows, not data rows, to summarize the current table
#' row/column context and operates on the level of the latest row split or the root of the table if no row splits have
#' occurred.
#'
#' If a column split has not yet been performed in the table, `col_split` must be set to `TRUE` for the first call of
#' [analyze_patients_exposure_in_cols()] or [summarize_patients_exposure_in_cols()].
#'
#' @inheritParams argument_convention
#' @param ex_var (`string`)\cr name of the variable in `df` containing exposure values.
#' @param custom_label (`string` or `NULL`)\cr if provided and `labelstr` is empty, this will be used as label.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("analyze_patients_exposure_in_cols"), type = "sh")``
#'
#' @name summarize_patients_exposure_in_cols
#' @order 1
NULL

#' @describeIn summarize_patients_exposure_in_cols Statistics function which counts numbers
#'   of patients and the sum of exposure across all patients.
#'
#' @return
#' * `s_count_patients_sum_exposure()` returns a named `list` with the statistics:
#'   * `n_patients`: Number of unique patients in `df`.
#'   * `sum_exposure`: Sum of `ex_var` across all patients in `df`.
#'
#' @keywords internal
s_count_patients_sum_exposure <- function(df,
                                          ex_var = "AVAL",
                                          id = "USUBJID",
                                          labelstr = "",
                                          .stats = c("n_patients", "sum_exposure"),
                                          .N_col, # nolint
                                          custom_label = NULL) {
  assert_df_with_variables(df, list(ex_var = ex_var, id = id))
  checkmate::assert_string(id)
  checkmate::assert_string(labelstr)
  checkmate::assert_string(custom_label, null.ok = TRUE)
  checkmate::assert_numeric(df[[ex_var]])
  checkmate::assert_true(all(.stats %in% c("n_patients", "sum_exposure")))

  row_label <- if (labelstr != "") {
    labelstr
  } else if (!is.null(custom_label)) {
    custom_label
  } else {
    "Total patients numbers/person time"
  }

  y <- list()

  if ("n_patients" %in% .stats) {
    y$n_patients <-
      formatters::with_label(
        s_num_patients_content(
          df = df,
          .N_col = .N_col, # nolint
          .var = id,
          labelstr = ""
        )$unique,
        row_label
      )
  }
  if ("sum_exposure" %in% .stats) {
    y$sum_exposure <- formatters::with_label(sum(df[[ex_var]]), row_label)
  }
  y
}

#' @describeIn summarize_patients_exposure_in_cols Analysis function which is used as `afun` in
#'   [rtables::analyze_colvars()] within `analyze_patients_exposure_in_cols()` and as `cfun` in
#'   [rtables::summarize_row_groups()] within `summarize_patients_exposure_in_cols()`.
#'
#' @return
#' * `a_count_patients_sum_exposure()` returns formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_patients_sum_exposure(
#'   df = df,
#'   var = "SEX",
#'   .N_col = nrow(df),
#'   .stats = "n_patients"
#' )
#'
#' @export
a_count_patients_sum_exposure <- function(df,
                                          var = NULL,
                                          ex_var = "AVAL",
                                          id = "USUBJID",
                                          add_total_level = FALSE,
                                          custom_label = NULL,
                                          labelstr = "",
                                          .N_col, # nolint
                                          .stats,
                                          .formats = list(n_patients = "xx (xx.x%)", sum_exposure = "xx")) {
  checkmate::assert_flag(add_total_level)

  if (!is.null(var)) {
    assert_df_with_variables(df, list(var = var))
    df[[var]] <- as.factor(df[[var]])
  }

  y <- list()
  if (is.null(var)) {
    y[[.stats]] <- list(Total = s_count_patients_sum_exposure(
      df = df,
      ex_var = ex_var,
      id = id,
      labelstr = labelstr,
      .N_col = .N_col,
      .stats = .stats,
      custom_label = custom_label
    )[[.stats]])
  } else {
    for (lvl in levels(df[[var]])) {
      y[[.stats]][[lvl]] <- s_count_patients_sum_exposure(
        df = subset(df, get(var) == lvl),
        ex_var = ex_var,
        id = id,
        labelstr = labelstr,
        .N_col = .N_col,
        .stats = .stats,
        custom_label = lvl
      )[[.stats]]
    }
    if (add_total_level) {
      y[[.stats]][["Total"]] <- s_count_patients_sum_exposure(
        df = df,
        ex_var = ex_var,
        id = id,
        labelstr = labelstr,
        .N_col = .N_col,
        .stats = .stats,
        custom_label = custom_label
      )[[.stats]]
    }
  }

  in_rows(.list = y[[.stats]], .formats = .formats[[.stats]])
}

#' @describeIn summarize_patients_exposure_in_cols Layout-creating function which can take statistics
#'   function arguments and additional format arguments. This function is a wrapper for
#'   [rtables::split_cols_by_multivar()] and [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_patients_exposure_in_cols()` returns a layout object suitable for passing to further
#'   layouting functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will
#'   add formatted content rows, with the statistics from `s_count_patients_sum_exposure()` arranged in
#'   columns, to the table layout.
#'
#' @examples
#' lyt5 <- basic_table() %>%
#'   summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE)
#'
#' result5 <- build_table(lyt5, df = df, alt_counts_df = adsl)
#' result5
#'
#' lyt6 <- basic_table() %>%
#'   summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE, .stats = "sum_exposure")
#'
#' result6 <- build_table(lyt6, df = df, alt_counts_df = adsl)
#' result6
#'
#' @export
#' @order 3
summarize_patients_exposure_in_cols <- function(lyt, # nolint
                                                var,
                                                ex_var = "AVAL",
                                                id = "USUBJID",
                                                add_total_level = FALSE,
                                                custom_label = NULL,
                                                col_split = TRUE,
                                                na_str = default_na_str(),
                                                ...,
                                                .stats = c("n_patients", "sum_exposure"),
                                                .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
                                                .indent_mods = NULL) {
  extra_args <- list(ex_var = ex_var, id = id, add_total_level = add_total_level, custom_label = custom_label, ...)

  if (col_split) {
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = rep(var, length(.stats)),
      varlabels = .labels[.stats],
      extra_args = list(.stats = .stats)
    )
  }
  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = a_count_patients_sum_exposure,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' @describeIn summarize_patients_exposure_in_cols Layout-creating function which can take statistics
#'   function arguments and additional format arguments. This function is a wrapper for
#'   [rtables::split_cols_by_multivar()] and [rtables::analyze_colvars()].
#'
#' @param col_split (`flag`)\cr whether the columns should be split. Set to `FALSE` when the required
#'   column split has been done already earlier in the layout pipe.
#'
#' @return
#' * `analyze_patients_exposure_in_cols()` returns a layout object suitable for passing to further
#'   layouting functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will
#'   add formatted data rows, with the statistics from `s_count_patients_sum_exposure()` arranged in
#'   columns, to the table layout.
#'
#' @note As opposed to [summarize_patients_exposure_in_cols()] which generates content rows,
#'   `analyze_patients_exposure_in_cols()` generates data rows which will _not_ be repeated on multiple
#'   pages when pagination is used.
#'
#' @examples
#' set.seed(1)
#' df <- data.frame(
#'   USUBJID = c(paste("id", seq(1, 12), sep = "")),
#'   ARMCD = c(rep("ARM A", 6), rep("ARM B", 6)),
#'   SEX = c(rep("Female", 6), rep("Male", 6)),
#'   AVAL = as.numeric(sample(seq(1, 20), 12)),
#'   stringsAsFactors = TRUE
#' )
#' adsl <- data.frame(
#'   USUBJID = c(paste("id", seq(1, 12), sep = "")),
#'   ARMCD = c(rep("ARM A", 2), rep("ARM B", 2)),
#'   SEX = c(rep("Female", 2), rep("Male", 2)),
#'   stringsAsFactors = TRUE
#' )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
#'   summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE) %>%
#'   analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE)
#' result <- build_table(lyt, df = df, alt_counts_df = adsl)
#' result
#'
#' lyt2 <- basic_table() %>%
#'   split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
#'   summarize_patients_exposure_in_cols(
#'     var = "AVAL", col_split = TRUE,
#'     .stats = "n_patients", custom_label = "some custom label"
#'   ) %>%
#'   analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE, ex_var = "AVAL")
#' result2 <- build_table(lyt2, df = df, alt_counts_df = adsl)
#' result2
#'
#' lyt3 <- basic_table() %>%
#'   analyze_patients_exposure_in_cols(var = "SEX", col_split = TRUE, ex_var = "AVAL")
#' result3 <- build_table(lyt3, df = df, alt_counts_df = adsl)
#' result3
#'
#' # Adding total levels and custom label
#' lyt4 <- basic_table(
#'   show_colcounts = TRUE
#' ) %>%
#'   analyze_patients_exposure_in_cols(
#'     var = "ARMCD",
#'     col_split = TRUE,
#'     add_total_level = TRUE,
#'     custom_label = "TOTAL"
#'   ) %>%
#'   append_topleft(c("", "Sex"))
#'
#' result4 <- build_table(lyt4, df = df, alt_counts_df = adsl)
#' result4
#'
#' @export
#' @order 2
analyze_patients_exposure_in_cols <- function(lyt, # nolint
                                              var = NULL,
                                              ex_var = "AVAL",
                                              id = "USUBJID",
                                              add_total_level = FALSE,
                                              custom_label = NULL,
                                              col_split = TRUE,
                                              na_str = default_na_str(),
                                              .stats = c("n_patients", "sum_exposure"),
                                              .labels = c(n_patients = "Patients", sum_exposure = "Person time"),
                                              .indent_mods = 0L,
                                              ...) {
  extra_args <- list(
    var = var, ex_var = ex_var, id = id, add_total_level = add_total_level, custom_label = custom_label, ...
  )

  if (col_split) {
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = rep(ex_var, length(.stats)),
      varlabels = .labels[.stats],
      extra_args = list(.stats = .stats)
    )
  }
  lyt <- lyt %>% analyze_colvars(
    afun = a_count_patients_sum_exposure,
    indent_mod = .indent_mods,
    na_str = na_str,
    extra_args = extra_args
  )
  lyt
}

#' Helper functions for multivariate logistic regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions used in calculations for logistic regression.
#'
#' @inheritParams argument_convention
#' @param fit_glm (`glm`)\cr logistic regression model fitted by [stats::glm()] with "binomial" family.
#'   Limited functionality is also available for conditional logistic regression models fitted by
#'   [survival::clogit()], currently this is used only by [extract_rsp_biomarkers()].
#' @param x (`character`)\cr a variable or interaction term in `fit_glm` (depending on the helper function used).
#'
#' @examples
#' library(dplyr)
#' library(broom)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' @name h_logistic_regression
NULL

#' @describeIn h_logistic_regression Helper function to extract interaction variable names from a fitted
#'   model assuming only one interaction term.
#'
#' @return Vector of names of interaction variables.
#'
#' @export
h_get_interaction_vars <- function(fit_glm) {
  checkmate::assert_class(fit_glm, "glm")
  terms_name <- attr(stats::terms(fit_glm), "term.labels")
  terms_order <- attr(stats::terms(fit_glm), "order")
  interaction_term <- terms_name[terms_order == 2]
  checkmate::assert_string(interaction_term)
  strsplit(interaction_term, split = ":")[[1]]
}

#' @describeIn h_logistic_regression Helper function to get the right coefficient name from the
#'   interaction variable names and the given levels. The main value here is that the order
#'   of first and second variable is checked in the `interaction_vars` input.
#'
#' @param interaction_vars (`character(2)`)\cr interaction variable names.
#' @param first_var_with_level (`character(2)`)\cr the first variable name with the interaction level.
#' @param second_var_with_level (`character(2)`)\cr the second variable name with the interaction level.
#'
#' @return Name of coefficient.
#'
#' @export
h_interaction_coef_name <- function(interaction_vars,
                                    first_var_with_level,
                                    second_var_with_level) {
  checkmate::assert_character(interaction_vars, len = 2, any.missing = FALSE)
  checkmate::assert_character(first_var_with_level, len = 2, any.missing = FALSE)
  checkmate::assert_character(second_var_with_level, len = 2, any.missing = FALSE)
  checkmate::assert_subset(c(first_var_with_level[1], second_var_with_level[1]), interaction_vars)

  first_name <- paste(first_var_with_level, collapse = "")
  second_name <- paste(second_var_with_level, collapse = "")
  if (first_var_with_level[1] == interaction_vars[1]) {
    paste(first_name, second_name, sep = ":")
  } else if (second_var_with_level[1] == interaction_vars[1]) {
    paste(second_name, first_name, sep = ":")
  }
}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates
#'   for the case when both the odds ratio and the interaction variable are categorical.
#'
#' @param odds_ratio_var (`string`)\cr the odds ratio variable.
#' @param interaction_var (`string`)\cr the interaction variable.
#'
#' @return Odds ratio.
#'
#' @export
h_or_cat_interaction <- function(odds_ratio_var,
                                 interaction_var,
                                 fit_glm,
                                 conf_level = 0.95) {
  interaction_vars <- h_get_interaction_vars(fit_glm)
  checkmate::assert_string(odds_ratio_var)
  checkmate::assert_string(interaction_var)
  checkmate::assert_subset(c(odds_ratio_var, interaction_var), interaction_vars)
  checkmate::assert_vector(interaction_vars, len = 2)

  xs_level <- fit_glm$xlevels
  xs_coef <- stats::coef(fit_glm)
  xs_vcov <- stats::vcov(fit_glm)
  y <- list()
  for (var_level in xs_level[[odds_ratio_var]][-1]) {
    x <- list()
    for (ref_level in xs_level[[interaction_var]]) {
      coef_names <- paste0(odds_ratio_var, var_level)
      if (ref_level != xs_level[[interaction_var]][1]) {
        interaction_coef_name <- h_interaction_coef_name(
          interaction_vars,
          c(odds_ratio_var, var_level),
          c(interaction_var, ref_level)
        )
        coef_names <- c(
          coef_names,
          interaction_coef_name
        )
      }
      if (length(coef_names) > 1) {
        ones <- t(c(1, 1))
        est <- as.numeric(ones %*% xs_coef[coef_names])
        se <- sqrt(as.numeric(ones %*% xs_vcov[coef_names, coef_names] %*% t(ones)))
      } else {
        est <- xs_coef[coef_names]
        se <- sqrt(as.numeric(xs_vcov[coef_names, coef_names]))
      }
      or <- exp(est)
      ci <- exp(est + c(lcl = -1, ucl = 1) * stats::qnorm((1 + conf_level) / 2) * se)
      x[[ref_level]] <- list(or = or, ci = ci)
    }
    y[[var_level]] <- x
  }
  y
}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates
#'   for the case when either the odds ratio or the interaction variable is continuous.
#'
#' @param at (`numeric` or `NULL`)\cr optional values for the interaction variable. Otherwise
#'   the median is used.
#'
#' @return Odds ratio.
#'
#' @note We don't provide a function for the case when both variables are continuous because
#'   this does not arise in this table, as the treatment arm variable will always be involved
#'   and categorical.
#'
#' @export
h_or_cont_interaction <- function(odds_ratio_var,
                                  interaction_var,
                                  fit_glm,
                                  at = NULL,
                                  conf_level = 0.95) {
  interaction_vars <- h_get_interaction_vars(fit_glm)
  checkmate::assert_string(odds_ratio_var)
  checkmate::assert_string(interaction_var)
  checkmate::assert_subset(c(odds_ratio_var, interaction_var), interaction_vars)
  checkmate::assert_vector(interaction_vars, len = 2)
  checkmate::assert_numeric(at, min.len = 1, null.ok = TRUE, any.missing = FALSE)
  xs_level <- fit_glm$xlevels
  xs_coef <- stats::coef(fit_glm)
  xs_vcov <- stats::vcov(fit_glm)
  xs_class <- attr(fit_glm$terms, "dataClasses")
  model_data <- fit_glm$model
  if (!is.null(at)) {
    checkmate::assert_set_equal(xs_class[interaction_var], "numeric")
  }
  y <- list()
  if (xs_class[interaction_var] == "numeric") {
    if (is.null(at)) {
      at <- ceiling(stats::median(model_data[[interaction_var]]))
    }

    for (var_level in xs_level[[odds_ratio_var]][-1]) {
      x <- list()
      for (increment in at) {
        coef_names <- paste0(odds_ratio_var, var_level)
        if (increment != 0) {
          interaction_coef_name <- h_interaction_coef_name(
            interaction_vars,
            c(odds_ratio_var, var_level),
            c(interaction_var, "")
          )
          coef_names <- c(
            coef_names,
            interaction_coef_name
          )
        }
        if (length(coef_names) > 1) {
          xvec <- t(c(1, increment))
          est <- as.numeric(xvec %*% xs_coef[coef_names])
          se <- sqrt(as.numeric(xvec %*% xs_vcov[coef_names, coef_names] %*% t(xvec)))
        } else {
          est <- xs_coef[coef_names]
          se <- sqrt(as.numeric(xs_vcov[coef_names, coef_names]))
        }
        or <- exp(est)
        ci <- exp(est + c(lcl = -1, ucl = 1) * stats::qnorm((1 + conf_level) / 2) * se)
        x[[as.character(increment)]] <- list(or = or, ci = ci)
      }
      y[[var_level]] <- x
    }
  } else {
    checkmate::assert_set_equal(xs_class[odds_ratio_var], "numeric")
    checkmate::assert_set_equal(xs_class[interaction_var], "factor")
    for (var_level in xs_level[[interaction_var]]) {
      coef_names <- odds_ratio_var
      if (var_level != xs_level[[interaction_var]][1]) {
        interaction_coef_name <- h_interaction_coef_name(
          interaction_vars,
          c(odds_ratio_var, ""),
          c(interaction_var, var_level)
        )
        coef_names <- c(
          coef_names,
          interaction_coef_name
        )
      }
      if (length(coef_names) > 1) {
        xvec <- t(c(1, 1))
        est <- as.numeric(xvec %*% xs_coef[coef_names])
        se <- sqrt(as.numeric(xvec %*% xs_vcov[coef_names, coef_names] %*% t(xvec)))
      } else {
        est <- xs_coef[coef_names]
        se <- sqrt(as.numeric(xs_vcov[coef_names, coef_names]))
      }
      or <- exp(est)
      ci <- exp(est + c(lcl = -1, ucl = 1) * stats::qnorm((1 + conf_level) / 2) * se)
      y[[var_level]] <- list(or = or, ci = ci)
    }
  }
  y
}

#' @describeIn h_logistic_regression Helper function to calculate the odds ratio estimates
#'   in case of an interaction. This is a wrapper for [h_or_cont_interaction()] and
#'   [h_or_cat_interaction()].
#'
#' @return Odds ratio.
#'
#' @export
h_or_interaction <- function(odds_ratio_var,
                             interaction_var,
                             fit_glm,
                             at = NULL,
                             conf_level = 0.95) {
  xs_class <- attr(fit_glm$terms, "dataClasses")
  if (any(xs_class[c(odds_ratio_var, interaction_var)] == "numeric")) {
    h_or_cont_interaction(
      odds_ratio_var,
      interaction_var,
      fit_glm,
      at = at,
      conf_level = conf_level
    )
  } else if (all(xs_class[c(odds_ratio_var, interaction_var)] == "factor")) {
    h_or_cat_interaction(
      odds_ratio_var,
      interaction_var,
      fit_glm,
      conf_level = conf_level
    )
  } else {
    stop("wrong interaction variable class, the interaction variable is not a numeric nor a factor")
  }
}

#' @describeIn h_logistic_regression Helper function to construct term labels from simple terms and the table
#'   of numbers of patients.
#'
#' @param terms (`character`)\cr simple terms.
#' @param table (`table`)\cr table containing numbers for terms.
#'
#' @return Term labels containing numbers of patients.
#'
#' @export
h_simple_term_labels <- function(terms,
                                 table) {
  checkmate::assert_true(is.table(table))
  checkmate::assert_multi_class(terms, classes = c("factor", "character"))
  terms <- as.character(terms)
  term_n <- table[terms]
  paste0(terms, ", n = ", term_n)
}

#' @describeIn h_logistic_regression Helper function to construct term labels from interaction terms and the table
#'   of numbers of patients.
#'
#' @param terms1 (`character`)\cr terms for first dimension (rows).
#' @param terms2 (`character`)\cr terms for second dimension (rows).
#' @param any (`flag`)\cr whether any of `term1` and `term2` can be fulfilled to count the
#'   number of patients. In that case they can only be scalar (strings).
#'
#' @return Term labels containing numbers of patients.
#'
#' @export
h_interaction_term_labels <- function(terms1,
                                      terms2,
                                      table,
                                      any = FALSE) {
  checkmate::assert_true(is.table(table))
  checkmate::assert_flag(any)
  checkmate::assert_multi_class(terms1, classes = c("factor", "character"))
  checkmate::assert_multi_class(terms2, classes = c("factor", "character"))
  terms1 <- as.character(terms1)
  terms2 <- as.character(terms2)
  if (any) {
    checkmate::assert_scalar(terms1)
    checkmate::assert_scalar(terms2)
    paste0(
      terms1, " or ", terms2, ", n = ",
      # Note that we double count in the initial sum the cell [terms1, terms2], therefore subtract.
      sum(c(table[terms1, ], table[, terms2])) - table[terms1, terms2]
    )
  } else {
    term_n <- table[cbind(terms1, terms2)]
    paste0(terms1, " * ", terms2, ", n = ", term_n)
  }
}

#' @describeIn h_logistic_regression Helper function to tabulate the main effect
#'   results of a (conditional) logistic regression model.
#'
#' @return Tabulated main effect results from a logistic regression model.
#'
#' @examples
#' h_glm_simple_term_extract("AGE", mod1)
#' h_glm_simple_term_extract("ARMCD", mod1)
#'
#' @export
h_glm_simple_term_extract <- function(x, fit_glm) {
  checkmate::assert_multi_class(fit_glm, c("glm", "clogit"))
  checkmate::assert_string(x)

  xs_class <- attr(fit_glm$terms, "dataClasses")
  xs_level <- fit_glm$xlevels
  xs_coef <- summary(fit_glm)$coefficients
  stats <- if (inherits(fit_glm, "glm")) {
    c("estimate" = "Estimate", "std_error" = "Std. Error", "pvalue" = "Pr(>|z|)")
  } else {
    c("estimate" = "coef", "std_error" = "se(coef)", "pvalue" = "Pr(>|z|)")
  }
  # Make sure x is not an interaction term.
  checkmate::assert_subset(x, names(xs_class))
  x_sel <- if (xs_class[x] == "numeric") x else paste0(x, xs_level[[x]][-1])
  x_stats <- as.data.frame(xs_coef[x_sel, stats, drop = FALSE], stringsAsFactors = FALSE)
  colnames(x_stats) <- names(stats)
  x_stats$estimate <- as.list(x_stats$estimate)
  x_stats$std_error <- as.list(x_stats$std_error)
  x_stats$pvalue <- as.list(x_stats$pvalue)
  x_stats$df <- as.list(1)
  if (xs_class[x] == "numeric") {
    x_stats$term <- x
    x_stats$term_label <- if (inherits(fit_glm, "glm")) {
      formatters::var_labels(fit_glm$data[x], fill = TRUE)
    } else {
      # We just fill in here with the `term` itself as we don't have the data available.
      x
    }
    x_stats$is_variable_summary <- FALSE
    x_stats$is_term_summary <- TRUE
  } else {
    checkmate::assert_class(fit_glm, "glm")
    # The reason is that we don't have the original data set in the `clogit` object
    # and therefore cannot determine the `x_numbers` here.
    x_numbers <- table(fit_glm$data[[x]])
    x_stats$term <- xs_level[[x]][-1]
    x_stats$term_label <- h_simple_term_labels(x_stats$term, x_numbers)
    x_stats$is_variable_summary <- FALSE
    x_stats$is_term_summary <- TRUE
    main_effects <- car::Anova(fit_glm, type = 3, test.statistic = "Wald")
    x_main <- data.frame(
      pvalue = main_effects[x, "Pr(>Chisq)", drop = TRUE],
      term = xs_level[[x]][1],
      term_label = paste("Reference", h_simple_term_labels(xs_level[[x]][1], x_numbers)),
      df = main_effects[x, "Df", drop = TRUE],
      stringsAsFactors = FALSE
    )
    x_main$pvalue <- as.list(x_main$pvalue)
    x_main$df <- as.list(x_main$df)
    x_main$estimate <- list(numeric(0))
    x_main$std_error <- list(numeric(0))
    if (length(xs_level[[x]][-1]) == 1) {
      x_main$pvalue <- list(numeric(0))
      x_main$df <- list(numeric(0))
    }
    x_main$is_variable_summary <- TRUE
    x_main$is_term_summary <- FALSE
    x_stats <- rbind(x_main, x_stats)
  }
  x_stats$variable <- x
  x_stats$variable_label <- if (inherits(fit_glm, "glm")) {
    formatters::var_labels(fit_glm$data[x], fill = TRUE)
  } else {
    x
  }
  x_stats$interaction <- ""
  x_stats$interaction_label <- ""
  x_stats$reference <- ""
  x_stats$reference_label <- ""
  rownames(x_stats) <- NULL
  x_stats[c(
    "variable",
    "variable_label",
    "term",
    "term_label",
    "interaction",
    "interaction_label",
    "reference",
    "reference_label",
    "estimate",
    "std_error",
    "df",
    "pvalue",
    "is_variable_summary",
    "is_term_summary"
  )]
}

#' @describeIn h_logistic_regression Helper function to tabulate the interaction term
#'   results of a logistic regression model.
#'
#' @return Tabulated interaction term results from a logistic regression model.
#'
#' @examples
#' h_glm_interaction_extract("ARMCD:AGE", mod2)
#'
#' @export
h_glm_interaction_extract <- function(x, fit_glm) {
  vars <- h_get_interaction_vars(fit_glm)
  xs_class <- attr(fit_glm$terms, "dataClasses")

  checkmate::assert_string(x)

  # Only take two-way interaction
  checkmate::assert_vector(vars, len = 2)

  # Only consider simple case: first variable in interaction is arm, a categorical variable
  checkmate::assert_disjunct(xs_class[vars[1]], "numeric")

  xs_level <- fit_glm$xlevels
  xs_coef <- summary(fit_glm)$coefficients
  main_effects <- car::Anova(fit_glm, type = 3, test.statistic = "Wald")
  stats <- c("estimate" = "Estimate", "std_error" = "Std. Error", "pvalue" = "Pr(>|z|)")
  v1_comp <- xs_level[[vars[1]]][-1]
  if (xs_class[vars[2]] == "numeric") {
    x_stats <- as.data.frame(
      xs_coef[paste0(vars[1], v1_comp, ":", vars[2]), stats, drop = FALSE],
      stringsAsFactors = FALSE
    )
    colnames(x_stats) <- names(stats)
    x_stats$term <- v1_comp
    x_numbers <- table(fit_glm$data[[vars[1]]])
    x_stats$term_label <- h_simple_term_labels(v1_comp, x_numbers)
    v1_ref <- xs_level[[vars[1]]][1]
    term_main <- v1_ref
    ref_label <- h_simple_term_labels(v1_ref, x_numbers)
  } else if (xs_class[vars[2]] != "numeric") {
    v2_comp <- xs_level[[vars[2]]][-1]
    v1_v2_grid <- expand.grid(v1 = v1_comp, v2 = v2_comp)
    x_sel <- paste(
      paste0(vars[1], v1_v2_grid$v1),
      paste0(vars[2], v1_v2_grid$v2),
      sep = ":"
    )
    x_stats <- as.data.frame(xs_coef[x_sel, stats, drop = FALSE], stringsAsFactors = FALSE)
    colnames(x_stats) <- names(stats)
    x_stats$term <- paste(v1_v2_grid$v1, "*", v1_v2_grid$v2)
    x_numbers <- table(fit_glm$data[[vars[1]]], fit_glm$data[[vars[2]]])
    x_stats$term_label <- h_interaction_term_labels(v1_v2_grid$v1, v1_v2_grid$v2, x_numbers)
    v1_ref <- xs_level[[vars[1]]][1]
    v2_ref <- xs_level[[vars[2]]][1]
    term_main <- paste(vars[1], vars[2], sep = " * ")
    ref_label <- h_interaction_term_labels(v1_ref, v2_ref, x_numbers, any = TRUE)
  }
  x_stats$df <- as.list(1)
  x_stats$pvalue <- as.list(x_stats$pvalue)
  x_stats$is_variable_summary <- FALSE
  x_stats$is_term_summary <- TRUE
  x_main <- data.frame(
    pvalue = main_effects[x, "Pr(>Chisq)", drop = TRUE],
    term = term_main,
    term_label = paste("Reference", ref_label),
    df = main_effects[x, "Df", drop = TRUE],
    stringsAsFactors = FALSE
  )
  x_main$pvalue <- as.list(x_main$pvalue)
  x_main$df <- as.list(x_main$df)
  x_main$estimate <- list(numeric(0))
  x_main$std_error <- list(numeric(0))
  x_main$is_variable_summary <- TRUE
  x_main$is_term_summary <- FALSE

  x_stats <- rbind(x_main, x_stats)
  x_stats$variable <- x
  x_stats$variable_label <- paste(
    "Interaction of",
    formatters::var_labels(fit_glm$data[vars[1]], fill = TRUE),
    "*",
    formatters::var_labels(fit_glm$data[vars[2]], fill = TRUE)
  )
  x_stats$interaction <- ""
  x_stats$interaction_label <- ""
  x_stats$reference <- ""
  x_stats$reference_label <- ""
  rownames(x_stats) <- NULL
  x_stats[c(
    "variable",
    "variable_label",
    "term",
    "term_label",
    "interaction",
    "interaction_label",
    "reference",
    "reference_label",
    "estimate",
    "std_error",
    "df",
    "pvalue",
    "is_variable_summary",
    "is_term_summary"
  )]
}

#' @describeIn h_logistic_regression Helper function to tabulate the interaction
#'   results of a logistic regression model. This basically is a wrapper for
#'   [h_or_interaction()] and [h_glm_simple_term_extract()] which puts the results
#'   in the right data frame format.
#'
#' @return A `data.frame` of tabulated interaction term results from a logistic regression model.
#'
#' @examples
#' h_glm_inter_term_extract("AGE", "ARMCD", mod2)
#'
#' @export
h_glm_inter_term_extract <- function(odds_ratio_var,
                                     interaction_var,
                                     fit_glm,
                                     ...) {
  # First obtain the main effects.
  main_stats <- h_glm_simple_term_extract(odds_ratio_var, fit_glm)
  main_stats$is_reference_summary <- FALSE
  main_stats$odds_ratio <- NA
  main_stats$lcl <- NA
  main_stats$ucl <- NA

  # Then we get the odds ratio estimates and put into df form.
  or_numbers <- h_or_interaction(odds_ratio_var, interaction_var, fit_glm, ...)
  is_num_or_var <- attr(fit_glm$terms, "dataClasses")[odds_ratio_var] == "numeric"

  if (is_num_or_var) {
    # Numeric OR variable case.
    references <- names(or_numbers)
    n_ref <- length(references)

    extract_from_list <- function(l, name, pos = 1) {
      unname(unlist(
        lapply(or_numbers, function(x) {
          x[[name]][pos]
        })
      ))
    }
    or_stats <- data.frame(
      variable = odds_ratio_var,
      variable_label = unname(formatters::var_labels(fit_glm$data[odds_ratio_var], fill = TRUE)),
      term = odds_ratio_var,
      term_label = unname(formatters::var_labels(fit_glm$data[odds_ratio_var], fill = TRUE)),
      interaction = interaction_var,
      interaction_label = unname(formatters::var_labels(fit_glm$data[interaction_var], fill = TRUE)),
      reference = references,
      reference_label = references,
      estimate = NA,
      std_error = NA,
      odds_ratio = extract_from_list(or_numbers, "or"),
      lcl = extract_from_list(or_numbers, "ci", pos = "lcl"),
      ucl = extract_from_list(or_numbers, "ci", pos = "ucl"),
      df = NA,
      pvalue = NA,
      is_variable_summary = FALSE,
      is_term_summary = FALSE,
      is_reference_summary = TRUE
    )
  } else {
    # Categorical OR variable case.
    references <- names(or_numbers[[1]])
    n_ref <- length(references)

    extract_from_list <- function(l, name, pos = 1) {
      unname(unlist(
        lapply(or_numbers, function(x) {
          lapply(x, function(y) y[[name]][pos])
        })
      ))
    }
    or_stats <- data.frame(
      variable = odds_ratio_var,
      variable_label = unname(formatters::var_labels(fit_glm$data[odds_ratio_var], fill = TRUE)),
      term = rep(names(or_numbers), each = n_ref),
      term_label = h_simple_term_labels(rep(names(or_numbers), each = n_ref), table(fit_glm$data[[odds_ratio_var]])),
      interaction = interaction_var,
      interaction_label = unname(formatters::var_labels(fit_glm$data[interaction_var], fill = TRUE)),
      reference = unlist(lapply(or_numbers, names)),
      reference_label = unlist(lapply(or_numbers, names)),
      estimate = NA,
      std_error = NA,
      odds_ratio = extract_from_list(or_numbers, "or"),
      lcl = extract_from_list(or_numbers, "ci", pos = "lcl"),
      ucl = extract_from_list(or_numbers, "ci", pos = "ucl"),
      df = NA,
      pvalue = NA,
      is_variable_summary = FALSE,
      is_term_summary = FALSE,
      is_reference_summary = TRUE
    )
  }

  df <- rbind(
    main_stats[, names(or_stats)],
    or_stats
  )
  df[order(-df$is_variable_summary, df$term, -df$is_term_summary, df$reference), ]
}

#' @describeIn h_logistic_regression Helper function to tabulate the results including
#'   odds ratios and confidence intervals of simple terms.
#'
#' @return Tabulated statistics for the given variable(s) from the logistic regression model.
#'
#' @examples
#' h_logistic_simple_terms("AGE", mod1)
#'
#' @export
h_logistic_simple_terms <- function(x, fit_glm, conf_level = 0.95) {
  checkmate::assert_multi_class(fit_glm, c("glm", "clogit"))
  if (inherits(fit_glm, "glm")) {
    checkmate::assert_set_equal(fit_glm$family$family, "binomial")
  }
  terms_name <- attr(stats::terms(fit_glm), "term.labels")
  xs_class <- attr(fit_glm$terms, "dataClasses")
  interaction <- terms_name[which(!terms_name %in% names(xs_class))]
  checkmate::assert_subset(x, terms_name)
  if (length(interaction) != 0) {
    # Make sure any item in x is not part of interaction term
    checkmate::assert_disjunct(x, unlist(strsplit(interaction, ":")))
  }
  x_stats <- lapply(x, h_glm_simple_term_extract, fit_glm)
  x_stats <- do.call(rbind, x_stats)
  q_norm <- stats::qnorm((1 + conf_level) / 2)
  x_stats$odds_ratio <- lapply(x_stats$estimate, exp)
  x_stats$lcl <- Map(function(or, se) exp(log(or) - q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  x_stats$ucl <- Map(function(or, se) exp(log(or) + q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  x_stats$ci <- Map(function(lcl, ucl) c(lcl, ucl), lcl = x_stats$lcl, ucl = x_stats$ucl)
  x_stats
}

#' @describeIn h_logistic_regression Helper function to tabulate the results including
#'   odds ratios and confidence intervals of interaction terms.
#'
#' @return Tabulated statistics for the given variable(s) from the logistic regression model.
#'
#' @examples
#' h_logistic_inter_terms(c("RACE", "AGE", "ARMCD", "AGE:ARMCD"), mod2)
#'
#' @export
h_logistic_inter_terms <- function(x,
                                   fit_glm,
                                   conf_level = 0.95,
                                   at = NULL) {
  # Find out the interaction variables and interaction term.
  inter_vars <- h_get_interaction_vars(fit_glm)
  checkmate::assert_vector(inter_vars, len = 2)


  inter_term_index <- intersect(grep(inter_vars[1], x), grep(inter_vars[2], x))
  inter_term <- x[inter_term_index]

  # For the non-interaction vars we need the standard stuff.
  normal_terms <- setdiff(x, union(inter_vars, inter_term))

  x_stats <- lapply(normal_terms, h_glm_simple_term_extract, fit_glm)
  x_stats <- do.call(rbind, x_stats)
  q_norm <- stats::qnorm((1 + conf_level) / 2)
  x_stats$odds_ratio <- lapply(x_stats$estimate, exp)
  x_stats$lcl <- Map(function(or, se) exp(log(or) - q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  x_stats$ucl <- Map(function(or, se) exp(log(or) + q_norm * se), x_stats$odds_ratio, x_stats$std_error)
  normal_stats <- x_stats
  normal_stats$is_reference_summary <- FALSE

  # Now the interaction term itself.
  inter_term_stats <- h_glm_interaction_extract(inter_term, fit_glm)
  inter_term_stats$odds_ratio <- NA
  inter_term_stats$lcl <- NA
  inter_term_stats$ucl <- NA
  inter_term_stats$is_reference_summary <- FALSE

  is_intervar1_numeric <- attr(fit_glm$terms, "dataClasses")[inter_vars[1]] == "numeric"

  # Interaction stuff.
  inter_stats_one <- h_glm_inter_term_extract(
    inter_vars[1],
    inter_vars[2],
    fit_glm,
    conf_level = conf_level,
    at = `if`(is_intervar1_numeric, NULL, at)
  )
  inter_stats_two <- h_glm_inter_term_extract(
    inter_vars[2],
    inter_vars[1],
    fit_glm,
    conf_level = conf_level,
    at = `if`(is_intervar1_numeric, at, NULL)
  )

  # Now just combine everything in one data frame.
  col_names <- c(
    "variable",
    "variable_label",
    "term",
    "term_label",
    "interaction",
    "interaction_label",
    "reference",
    "reference_label",
    "estimate",
    "std_error",
    "df",
    "pvalue",
    "odds_ratio",
    "lcl",
    "ucl",
    "is_variable_summary",
    "is_term_summary",
    "is_reference_summary"
  )
  df <- rbind(
    inter_stats_one[, col_names],
    inter_stats_two[, col_names],
    inter_term_stats[, col_names]
  )
  if (length(normal_terms) > 0) {
    df <- rbind(
      normal_stats[, col_names],
      df
    )
  }
  df$ci <- combine_vectors(df$lcl, df$ucl)
  df
}

#' Kaplan-Meier plot
#'
#' @description `r lifecycle::badge("stable")`
#'
#' From a survival model, a graphic is rendered along with tabulated annotation
#' including the number of patient at risk at given time and the median survival
#' per group.
#'
#' @inheritParams argument_convention
#' @param variables (named `list`)\cr variable names. Details are:
#'   * `tte` (`numeric`)\cr variable indicating time-to-event duration values.
#'   * `is_event` (`logical`)\cr event variable. `TRUE` if event, `FALSE` if time to event is censored.
#'   * `arm` (`factor`)\cr the treatment group variable.
#'   * `strata` (`character` or `NULL`)\cr variable names indicating stratification factors.
#' @param control_surv (`list`)\cr parameters for comparison details, specified by using
#'   the helper function [control_surv_timepoint()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for survival rate.
#'   * `conf_type` (`string`)\cr `"plain"` (default), `"log"`, `"log-log"` for confidence interval type,
#'     see more in [survival::survfit()]. Note that the option "none" is no longer supported.
#' @param col (`character`)\cr lines colors. Length of a vector should be equal
#'   to number of strata from [survival::survfit()].
#' @param lty (`numeric`)\cr line type. If a vector is given, its length should be equal to the number of strata from
#'   [survival::survfit()].
#' @param lwd (`numeric`)\cr line width. If a vector is given, its length should be equal to the number of strata from
#'   [survival::survfit()].
#' @param censor_show (`flag`)\cr whether to show censored observations.
#' @param pch (`string`)\cr name of symbol or character to use as point symbol to indicate censored cases.
#' @param size (`numeric(1)`)\cr size of censored point symbols.
#' @param max_time (`numeric(1)`)\cr maximum value to show on x-axis. Only data values less than or up to
#'   this threshold value will be plotted (defaults to `NULL`).
#' @param xticks (`numeric` or `NULL`)\cr numeric vector of tick positions or a single number with spacing
#'   between ticks on the x-axis. If `NULL` (default), [labeling::extended()] is used to determine
#'   optimal tick positions on the x-axis.
#' @param xlab (`string`)\cr x-axis label.
#' @param yval (`string`)\cr type of plot, to be plotted on the y-axis. Options are `Survival` (default) and `Failure`
#'   probability.
#' @param ylab (`string`)\cr y-axis label.
#' @param title (`string`)\cr plot title.
#' @param footnotes (`string`)\cr plot footnotes.
#' @param font_size (`numeric(1)`)\cr font size to use for all text.
#' @param ci_ribbon (`flag`)\cr whether the confidence interval should be drawn around the Kaplan-Meier curve.
#' @param annot_at_risk (`flag`)\cr compute and add the annotation table reporting the number of patient at risk
#'   matching the main grid of the Kaplan-Meier curve.
#' @param annot_at_risk_title (`flag`)\cr whether the "Patients at Risk" title should be added above the `annot_at_risk`
#'   table. Has no effect if `annot_at_risk` is `FALSE`. Defaults to `TRUE`.
#' @param annot_surv_med (`flag`)\cr compute and add the annotation table on the Kaplan-Meier curve estimating the
#'   median survival time per group.
#' @param annot_coxph (`flag`)\cr whether to add the annotation table from a [survival::coxph()] model.
#' @param annot_stats (`string` or `NULL`)\cr statistics annotations to add to the plot. Options are
#'   `median` (median survival follow-up time) and `min` (minimum survival follow-up time).
#' @param annot_stats_vlines (`flag`)\cr add vertical lines corresponding to each of the statistics
#'   specified by `annot_stats`. If `annot_stats` is `NULL` no lines will be added.
#' @param control_coxph_pw (`list`)\cr parameters for comparison details, specified using the helper function
#'   [control_coxph()]. Some possible parameter options are:
#'   * `pval_method` (`string`)\cr p-value method for testing hazard ratio = 1.
#'     Default method is `"log-rank"`, can also be set to `"wald"` or `"likelihood"`.
#'   * `ties` (`string`)\cr method for tie handling. Default is `"efron"`,
#'     can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()]
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for HR.
#' @param ref_group_coxph (`string` or `NULL`)\cr level of arm variable to use as reference group in calculations for
#'   `annot_coxph` table. If `NULL` (default), uses the first level of the arm variable.
#' @param control_annot_surv_med (`list`)\cr parameters to control the position and size of the annotation table added
#'   to the plot when `annot_surv_med = TRUE`, specified using the [control_surv_med_annot()] function. Parameter
#'   options are: `x`, `y`, `w`, `h`, and `fill`. See [control_surv_med_annot()] for details.
#' @param control_annot_coxph (`list`)\cr parameters to control the position and size of the annotation table added
#'   to the plot when `annot_coxph = TRUE`, specified using the [control_coxph_annot()] function. Parameter
#'   options are: `x`, `y`, `w`, `h`, `fill`, and `ref_lbls`. See [control_coxph_annot()] for details.
#' @param legend_pos (`numeric(2)` or `NULL`)\cr vector containing x- and y-coordinates, respectively, for the legend
#'   position relative to the KM plot area. If `NULL` (default), the legend is positioned in the bottom right corner of
#'   the plot, or the middle right of the plot if needed to prevent overlapping.
#' @param rel_height_plot (`proportion`)\cr proportion of total figure height to allocate to the Kaplan-Meier plot.
#'   Relative height of patients at risk table is then `1 - rel_height_plot`. If `annot_at_risk = FALSE` or
#'   `as_list = TRUE`, this parameter is ignored.
#' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to format the Kaplan-Meier plot.
#' @param as_list (`flag`)\cr whether the two `ggplot` objects should be returned as a list when `annot_at_risk = TRUE`.
#'   If `TRUE`, a named list with two elements, `plot` and `table`, will be returned. If `FALSE` (default) the patients
#'   at risk table is printed below the plot via [cowplot::plot_grid()].
#' @param draw `r lifecycle::badge("deprecated")` This function no longer generates `grob` objects.
#' @param newpage `r lifecycle::badge("deprecated")` This function no longer generates `grob` objects.
#' @param gp `r lifecycle::badge("deprecated")` This function no longer generates `grob` objects.
#' @param vp `r lifecycle::badge("deprecated")` This function no longer generates `grob` objects.
#' @param name `r lifecycle::badge("deprecated")` This function no longer generates `grob` objects.
#' @param annot_coxph_ref_lbls `r lifecycle::badge("deprecated")` Please use the `ref_lbls` element of
#'   `control_annot_coxph` instead.
#' @param position_coxph `r lifecycle::badge("deprecated")`  Please use the `x` and `y` elements of
#'   `control_annot_coxph` instead.
#' @param position_surv_med `r lifecycle::badge("deprecated")` Please use the `x` and `y` elements of
#'   `control_annot_surv_med` instead.
#' @param width_annots `r lifecycle::badge("deprecated")` Please use the `w` element of `control_annot_surv_med`
#'   (for `surv_med`) and `control_annot_coxph` (for `coxph`)."
#'
#' @return A `ggplot` Kaplan-Meier plot and (optionally) summary table.
#'
#' @examples
#' library(dplyr)
#'
#' df <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#' variables <- list(tte = "AVAL", is_event = "is_event", arm = "ARMCD")
#'
#' # Basic examples
#' g_km(df = df, variables = variables)
#' g_km(df = df, variables = variables, yval = "Failure")
#'
#' # Examples with customization parameters applied
#' g_km(
#'   df = df,
#'   variables = variables,
#'   control_surv = control_surv_timepoint(conf_level = 0.9),
#'   col = c("grey25", "grey50", "grey75"),
#'   annot_at_risk_title = FALSE,
#'   lty = 1:3,
#'   font_size = 8
#' )
#' g_km(
#'   df = df,
#'   variables = variables,
#'   annot_stats = c("min", "median"),
#'   annot_stats_vlines = TRUE,
#'   max_time = 3000,
#'   ggtheme = ggplot2::theme_minimal()
#' )
#'
#' # Example with pairwise Cox-PH analysis annotation table, adjusted annotation tables
#' g_km(
#'   df = df, variables = variables,
#'   annot_coxph = TRUE,
#'   control_coxph = control_coxph(pval_method = "wald", ties = "exact", conf_level = 0.99),
#'   control_annot_coxph = control_coxph_annot(x = 0.26, w = 0.35),
#'   control_annot_surv_med = control_surv_med_annot(x = 0.8, y = 0.9, w = 0.35)
#' )
#'
#' @aliases kaplan_meier
#' @export
g_km <- function(df,
                 variables,
                 control_surv = control_surv_timepoint(),
                 col = NULL,
                 lty = NULL,
                 lwd = 0.5,
                 censor_show = TRUE,
                 pch = 3,
                 size = 2,
                 max_time = NULL,
                 xticks = NULL,
                 xlab = "Days",
                 yval = c("Survival", "Failure"),
                 ylab = paste(yval, "Probability"),
                 ylim = NULL,
                 title = NULL,
                 footnotes = NULL,
                 font_size = 10,
                 ci_ribbon = FALSE,
                 annot_at_risk = TRUE,
                 annot_at_risk_title = TRUE,
                 annot_surv_med = TRUE,
                 annot_coxph = FALSE,
                 annot_stats = NULL,
                 annot_stats_vlines = FALSE,
                 control_coxph_pw = control_coxph(),
                 ref_group_coxph = NULL,
                 control_annot_surv_med = control_surv_med_annot(),
                 control_annot_coxph = control_coxph_annot(),
                 legend_pos = NULL,
                 rel_height_plot = 0.75,
                 ggtheme = NULL,
                 as_list = FALSE,
                 draw = lifecycle::deprecated(),
                 newpage = lifecycle::deprecated(),
                 gp = lifecycle::deprecated(),
                 vp = lifecycle::deprecated(),
                 name = lifecycle::deprecated(),
                 annot_coxph_ref_lbls = lifecycle::deprecated(),
                 position_coxph = lifecycle::deprecated(),
                 position_surv_med = lifecycle::deprecated(),
                 width_annots = lifecycle::deprecated()) {
  # Deprecated argument warnings
  if (lifecycle::is_present(draw)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(draw)",
      details = "This argument is no longer used since the plot is now generated as a `ggplot2` object."
    )
  }
  if (lifecycle::is_present(newpage)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(newpage)",
      details = "This argument is no longer used since the plot is now generated as a `ggplot2` object."
    )
  }
  if (lifecycle::is_present(gp)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(gp)",
      details = "This argument is no longer used since the plot is now generated as a `ggplot2` object."
    )
  }
  if (lifecycle::is_present(vp)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(vp)",
      details = "This argument is no longer used since the plot is now generated as a `ggplot2` object."
    )
  }
  if (lifecycle::is_present(name)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(name)",
      details = "This argument is no longer used since the plot is now generated as a `ggplot2` object."
    )
  }
  if (lifecycle::is_present(annot_coxph_ref_lbls)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(annot_coxph_ref_lbls)",
      details = "Please specify this setting using the 'ref_lbls' element of control_annot_coxph."
    )
    control_annot_coxph[["ref_lbls"]] <- annot_coxph_ref_lbls
  }
  if (lifecycle::is_present(position_coxph)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(position_coxph)",
      details = "Please specify this setting using the 'x' and 'y' elements of control_annot_coxph."
    )
    control_annot_coxph[["x"]] <- position_coxph[1]
    control_annot_coxph[["y"]] <- position_coxph[2]
  }
  if (lifecycle::is_present(position_surv_med)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(position_surv_med)",
      details = "Please specify this setting using the 'x' and 'y' elements of control_annot_surv_med."
    )
    control_annot_surv_med[["x"]] <- position_surv_med[1]
    control_annot_surv_med[["y"]] <- position_surv_med[2]
  }
  if (lifecycle::is_present(width_annots)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_km(width_annots)",
      details = paste(
        "Please specify widths of annotation tables relative to the plot area using the 'w' element of",
        "control_annot_surv_med (for surv_med) and control_annot_coxph (for coxph)."
      )
    )
    control_annot_surv_med[["w"]] <- as.numeric(width_annots[["surv_med"]])
    control_annot_coxph[["w"]] <- as.numeric(width_annots[["coxph"]])
  }

  checkmate::assert_list(variables)
  checkmate::assert_subset(c("tte", "arm", "is_event"), names(variables))
  checkmate::assert_logical(censor_show, len = 1)
  checkmate::assert_numeric(size, len = 1)
  checkmate::assert_numeric(max_time, len = 1, null.ok = TRUE)
  checkmate::assert_numeric(xticks, null.ok = TRUE)
  checkmate::assert_character(xlab, len = 1, null.ok = TRUE)
  checkmate::assert_character(yval)
  checkmate::assert_character(ylab, null.ok = TRUE)
  checkmate::assert_numeric(ylim, finite = TRUE, any.missing = FALSE, len = 2, sorted = TRUE, null.ok = TRUE)
  checkmate::assert_character(title, len = 1, null.ok = TRUE)
  checkmate::assert_character(footnotes, len = 1, null.ok = TRUE)
  checkmate::assert_numeric(font_size, len = 1)
  checkmate::assert_logical(ci_ribbon, len = 1)
  checkmate::assert_logical(annot_at_risk, len = 1)
  checkmate::assert_logical(annot_at_risk_title, len = 1)
  checkmate::assert_logical(annot_surv_med, len = 1)
  checkmate::assert_logical(annot_coxph, len = 1)
  checkmate::assert_subset(annot_stats, c("median", "min"))
  checkmate::assert_logical(annot_stats_vlines)
  checkmate::assert_list(control_coxph_pw)
  checkmate::assert_character(ref_group_coxph, len = 1, null.ok = TRUE)
  checkmate::assert_list(control_annot_surv_med)
  checkmate::assert_list(control_annot_coxph)
  checkmate::assert_numeric(legend_pos, finite = TRUE, any.missing = FALSE, len = 2, null.ok = TRUE)
  assert_proportion_value(rel_height_plot)
  checkmate::assert_logical(as_list)

  tte <- variables$tte
  is_event <- variables$is_event
  arm <- variables$arm
  assert_valid_factor(df[[arm]])
  armval <- as.character(unique(df[[arm]]))
  assert_df_with_variables(df, list(tte = tte, is_event = is_event, arm = arm))
  checkmate::assert_logical(df[[is_event]], min.len = 1)
  checkmate::assert_numeric(df[[tte]], min.len = 1)
  checkmate::assert_vector(col, len = length(armval), null.ok = TRUE)
  checkmate::assert_vector(lty, null.ok = TRUE)
  checkmate::assert_numeric(lwd, len = 1, null.ok = TRUE)

  if (annot_coxph && length(armval) < 2) {
    stop(paste(
      "When `annot_coxph` = TRUE, `df` must contain at least 2 levels of `variables$arm`",
      "in order to calculate the hazard ratio."
    ))
  }

  # process model
  yval <- match.arg(yval)
  formula <- stats::as.formula(paste0("survival::Surv(", tte, ", ", is_event, ") ~ ", arm))
  fit_km <- survival::survfit(
    formula = formula,
    data = df,
    conf.int = control_surv$conf_level,
    conf.type = control_surv$conf_type
  )
  data <- h_data_plot(fit_km, armval = armval, max_time = max_time)

  # calculate x-ticks
  xticks <- h_xticks(data = data, xticks = xticks, max_time = max_time)

  # change estimates of survival to estimates of failure (1 - survival)
  if (yval == "Failure") {
    data[c("estimate", "conf.low", "conf.high", "censor")] <- list(
      1 - data$estimate, 1 - data$conf.low, 1 - data$conf.high, 1 - data$censor
    )
  }

  # derive y-axis limits
  if (is.null(ylim)) {
    if (!is.null(max_time)) {
      y_lwr <- min(data[data$time < max_time, ][["estimate"]])
      y_upr <- max(data[data$time < max_time, ][["estimate"]])
    } else {
      y_lwr <- min(data[["estimate"]])
      y_upr <- max(data[["estimate"]])
    }
    ylim <- c(y_lwr, y_upr)
  }

  # initialize ggplot
  gg_plt <- ggplot(
    data = data,
    mapping = aes(
      x = .data[["time"]],
      y = .data[["estimate"]],
      ymin = .data[["conf.low"]],
      ymax = .data[["conf.high"]],
      color = .data[["strata"]],
      fill = .data[["strata"]]
    )
  ) +
    theme_bw(base_size = font_size) +
    scale_y_continuous(limits = ylim, expand = c(0.025, 0)) +
    labs(title = title, x = xlab, y = ylab, caption = footnotes) +
    theme(
      axis.text = element_text(size = font_size),
      axis.title = element_text(size = font_size),
      legend.title = element_blank(),
      legend.text = element_text(size = font_size),
      legend.box.background = element_rect(fill = "white", linewidth = 0.5),
      legend.background = element_blank(),
      legend.position = "inside",
      legend.spacing.y = unit(-0.02, "npc"),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank()
    )

  # derive x-axis limits
  if (!is.null(max_time) && !is.null(xticks)) {
    gg_plt <- gg_plt + scale_x_continuous(
      breaks = xticks, limits = c(min(0, xticks), max(c(xticks, max_time))), expand = c(0.025, 0)
    )
  } else if (!is.null(xticks)) {
    if (max(data$time) <= max(xticks)) {
      gg_plt <- gg_plt + scale_x_continuous(
        breaks = xticks, limits = c(min(0, min(xticks)), max(xticks)), expand = c(0.025, 0)
      )
    } else {
      gg_plt <- gg_plt + scale_x_continuous(breaks = xticks, expand = c(0.025, 0))
    }
  } else if (!is.null(max_time)) {
    gg_plt <- gg_plt + scale_x_continuous(limits = c(0, max_time), expand = c(0.025, 0))
  }

  # set legend position
  if (!is.null(legend_pos)) {
    gg_plt <- gg_plt + theme(legend.position.inside = legend_pos)
  } else {
    max_time2 <- sort(
      data$time,
      partial = nrow(data) - length(armval) - 1
    )[nrow(data) - length(armval) - 1]

    y_rng <- ylim[2] - ylim[1]

    if (yval == "Survival" && all(data$estimate[data$time == max_time2] > ylim[1] + 0.09 * y_rng) &&
      all(data$estimate[data$time == max_time2] < ylim[1] + 0.5 * y_rng)) { # nolint
      gg_plt <- gg_plt +
        theme(
          legend.position.inside = c(1, 0.5),
          legend.justification = c(1.1, 0.6)
        )
    } else {
      gg_plt <- gg_plt +
        theme(
          legend.position.inside = c(1, 0),
          legend.justification = c(1.1, -0.4)
        )
    }
  }

  # add lines
  gg_plt <- if (is.null(lty)) {
    gg_plt + geom_step(linewidth = lwd, na.rm = TRUE)
  } else if (length(lty) == 1) {
    gg_plt + geom_step(linewidth = lwd, lty = lty, na.rm = TRUE)
  } else {
    gg_plt +
      geom_step(aes(lty = .data[["strata"]]), linewidth = lwd, na.rm = TRUE) +
      scale_linetype_manual(values = lty)
  }

  # add censor marks
  if (censor_show) {
    gg_plt <- gg_plt + geom_point(
      data = data[data$n.censor != 0, ],
      aes(x = .data[["time"]], y = .data[["censor"]], shape = "Censored"),
      size = size,
      na.rm = TRUE
    ) +
      scale_shape_manual(name = NULL, values = pch) +
      guides(fill = guide_legend(override.aes = list(shape = NA)))
  }

  # add ci ribbon
  if (ci_ribbon) gg_plt <- gg_plt + geom_ribbon(alpha = 0.3, lty = 0, na.rm = TRUE)

  # control aesthetics
  if (!is.null(col)) {
    gg_plt <- gg_plt +
      scale_color_manual(values = col) +
      scale_fill_manual(values = col)
  }
  if (!is.null(ggtheme)) gg_plt <- gg_plt + ggtheme

  # annotate with stats (text/vlines)
  if (!is.null(annot_stats)) {
    if ("median" %in% annot_stats) {
      fit_km_all <- survival::survfit(
        formula = stats::as.formula(paste0("survival::Surv(", tte, ", ", is_event, ") ~ ", 1)),
        data = df,
        conf.int = control_surv$conf_level,
        conf.type = control_surv$conf_type
      )
      gg_plt <- gg_plt +
        annotate(
          "text",
          size = font_size / .pt, col = 1, lineheight = 0.95,
          x = stats::median(fit_km_all) + 0.07 * max(data$time),
          y = ifelse(yval == "Survival", 0.65, 0.35),
          label = paste("Median F/U:\n", round(stats::median(fit_km_all), 1), tolower(df$AVALU[1]))
        )
      if (annot_stats_vlines) {
        gg_plt <- gg_plt +
          annotate(
            "segment",
            x = stats::median(fit_km_all), xend = stats::median(fit_km_all), y = -Inf, yend = Inf,
            linetype = 2, col = "darkgray"
          )
      }
    }
    if ("min" %in% annot_stats) {
      min_fu <- min(df[[tte]])
      gg_plt <- gg_plt +
        annotate(
          "text",
          size = font_size / .pt, col = 1, lineheight = 0.95,
          x = min_fu + max(data$time) * 0.07,
          y = ifelse(yval == "Survival", 0.96, 0.05),
          label = paste("Min. F/U:\n", round(min_fu, 1), tolower(df$AVALU[1]))
        )
      if (annot_stats_vlines) {
        gg_plt <- gg_plt +
          annotate(
            "segment",
            linetype = 2, col = "darkgray",
            x = min_fu, xend = min_fu, y = Inf, yend = -Inf
          )
      }
    }
    gg_plt <- gg_plt + guides(fill = guide_legend(override.aes = list(shape = NA, label = "")))
  }

  # add at risk annotation table
  if (annot_at_risk) {
    annot_tbl <- summary(fit_km, times = xticks, extend = TRUE)
    annot_tbl <- if (is.null(fit_km$strata)) {
      data.frame(
        n.risk = annot_tbl$n.risk,
        time = annot_tbl$time,
        strata = armval
      )
    } else {
      strata_lst <- strsplit(sub("=", "equals", levels(annot_tbl$strata)), "equals")
      levels(annot_tbl$strata) <- matrix(unlist(strata_lst), ncol = 2, byrow = TRUE)[, 2]
      data.frame(
        n.risk = annot_tbl$n.risk,
        time = annot_tbl$time,
        strata = annot_tbl$strata
      )
    }

    at_risk_tbl <- as.data.frame(tidyr::pivot_wider(annot_tbl, names_from = "time", values_from = "n.risk")[, -1])
    at_risk_tbl[is.na(at_risk_tbl)] <- 0
    rownames(at_risk_tbl) <- levels(annot_tbl$strata)

    gg_at_risk <- df2gg(
      at_risk_tbl,
      font_size = font_size, col_labels = FALSE, hline = FALSE,
      colwidths = rep(1, ncol(at_risk_tbl))
    ) +
      labs(title = if (annot_at_risk_title) "Patients at Risk:" else NULL, x = xlab) +
      theme_bw(base_size = font_size) +
      theme(
        plot.title = element_text(size = font_size, vjust = 3, face = "bold"),
        panel.border = element_blank(),
        panel.grid = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.y = element_text(size = font_size, face = "italic", hjust = 1),
        axis.text.x = element_text(size = font_size),
        axis.line.x = element_line()
      ) +
      coord_cartesian(clip = "off", ylim = c(0.5, nrow(at_risk_tbl)))
    gg_at_risk <- suppressMessages(
      gg_at_risk +
        scale_x_continuous(expand = c(0.025, 0), breaks = seq_along(at_risk_tbl) - 0.5, labels = xticks) +
        scale_y_continuous(labels = rev(levels(annot_tbl$strata)), breaks = seq_len(nrow(at_risk_tbl)))
    )

    if (!as_list) {
      gg_plt <- cowplot::plot_grid(
        gg_plt,
        gg_at_risk,
        align = "v",
        axis = "tblr",
        ncol = 1,
        rel_heights = c(rel_height_plot, 1 - rel_height_plot)
      )
    }
  }

  # add median survival time annotation table
  if (annot_surv_med) {
    surv_med_tbl <- h_tbl_median_surv(fit_km = fit_km, armval = armval)
    bg_fill <- if (isTRUE(control_annot_surv_med[["fill"]])) "#00000020" else control_annot_surv_med[["fill"]]

    gg_surv_med <- df2gg(surv_med_tbl, font_size = font_size, colwidths = c(1, 1, 2), bg_fill = bg_fill) +
      theme(
        axis.text.y = element_text(size = font_size, face = "italic", hjust = 1),
        plot.margin = margin(0, 2, 0, 5)
      ) +
      coord_cartesian(clip = "off", ylim = c(0.5, nrow(surv_med_tbl) + 1.5))
    gg_surv_med <- suppressMessages(
      gg_surv_med +
        scale_x_continuous(expand = c(0.025, 0)) +
        scale_y_continuous(labels = rev(rownames(surv_med_tbl)), breaks = seq_len(nrow(surv_med_tbl)))
    )

    gg_plt <- cowplot::ggdraw(gg_plt) +
      cowplot::draw_plot(
        gg_surv_med,
        control_annot_surv_med[["x"]],
        control_annot_surv_med[["y"]],
        width = control_annot_surv_med[["w"]],
        height = control_annot_surv_med[["h"]],
        vjust = 0.5,
        hjust = 0.5
      )
  }

  # add coxph annotation table
  if (annot_coxph) {
    coxph_tbl <- h_tbl_coxph_pairwise(
      df = df,
      variables = variables,
      ref_group_coxph = ref_group_coxph,
      control_coxph_pw = control_coxph_pw,
      annot_coxph_ref_lbls = control_annot_coxph[["ref_lbls"]]
    )
    bg_fill <- if (isTRUE(control_annot_coxph[["fill"]])) "#00000020" else control_annot_coxph[["fill"]]

    gg_coxph <- df2gg(coxph_tbl, font_size = font_size, colwidths = c(1.1, 1, 3), bg_fill = bg_fill) +
      theme(
        axis.text.y = element_text(size = font_size, face = "italic", hjust = 1),
        plot.margin = margin(0, 2, 0, 5)
      ) +
      coord_cartesian(clip = "off", ylim = c(0.5, nrow(coxph_tbl) + 1.5))
    gg_coxph <- suppressMessages(
      gg_coxph +
        scale_x_continuous(expand = c(0.025, 0)) +
        scale_y_continuous(labels = rev(rownames(coxph_tbl)), breaks = seq_len(nrow(coxph_tbl)))
    )

    gg_plt <- cowplot::ggdraw(gg_plt) +
      cowplot::draw_plot(
        gg_coxph,
        control_annot_coxph[["x"]],
        control_annot_coxph[["y"]],
        width = control_annot_coxph[["w"]],
        height = control_annot_coxph[["h"]],
        vjust = 0.5,
        hjust = 0.5
      )
  }

  if (as_list) {
    list(plot = gg_plt, table = gg_at_risk)
  } else {
    gg_plt
  }
}

#' Analyze numeric variables in columns
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' The layout-creating function [analyze_vars_in_cols()] creates a layout element to generate a column-wise
#' analysis table.
#'
#' This function sets the analysis methods as column labels and is a wrapper for [rtables::analyze_colvars()].
#' It was designed principally for PK tables.
#'
#' @inheritParams argument_convention
#' @inheritParams rtables::analyze_colvars
#' @param imp_rule (`string` or `NULL`)\cr imputation rule setting. Defaults to `NULL` for no imputation rule. Can
#'   also be `"1/3"` to implement 1/3 imputation rule or `"1/2"` to implement 1/2 imputation rule. In order
#'   to use an imputation rule, the `avalcat_var` argument must be specified. See [imputation_rule()]
#'   for more details on imputation.
#' @param avalcat_var (`string`)\cr if `imp_rule` is not `NULL`, name of variable that indicates whether a
#'   row in the data corresponds to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of
#'   the above (defaults to `"AVALCAT1"`). Variable must be present in the data and should match the variable
#'   used to calculate the `n_blq` statistic (if included in `.stats`).
#' @param cache (`flag`)\cr whether to store computed values in a temporary caching environment. This will
#'   speed up calculations in large tables, but should be set to `FALSE` if the same `rtable` layout is
#'   used for multiple tables with different data. Defaults to `FALSE`.
#' @param row_labels (`character`)\cr as this function works in columns space, usually `.labels`
#'   character vector applies on the column space. You can change the row labels by defining this
#'   parameter to a named character vector with names corresponding to the split values. It defaults
#'   to `NULL` and if it contains only one `string`, it will duplicate that as a row label.
#' @param do_summarize_row_groups (`flag`)\cr defaults to `FALSE` and applies the analysis to the current
#'   label rows. This is a wrapper of [rtables::summarize_row_groups()] and it can accept `labelstr`
#'   to define row labels. This behavior is not supported as we never need to overload row labels.
#' @param split_col_vars (`flag`)\cr defaults to `TRUE` and puts the analysis results onto the columns.
#'   This option allows you to add multiple instances of this functions, also in a nested fashion,
#'   without adding more splits. This split must happen only one time on a single layout.
#'
#' @return
#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output
#' in columns, and add it to the table layout.
#'
#' @note
#' * This is an experimental implementation of [rtables::summarize_row_groups()] and [rtables::analyze_colvars()]
#'   that may be subjected to changes as `rtables` extends its support to more complex analysis pipelines in the
#'   column space. We encourage users to read the examples carefully and file issues for different use cases.
#' * In this function, `labelstr` behaves atypically. If `labelstr = NULL` (the default), row labels are assigned
#'   automatically as the split values if `do_summarize_row_groups = FALSE` (the default), and as the group label
#'   if `do_summarize_row_groups = TRUE`.
#'
#' @seealso [analyze_vars()], [rtables::analyze_colvars()].
#'
#' @examples
#' library(dplyr)
#'
#' # Data preparation
#' adpp <- tern_ex_adpp %>% h_pkparam_sort()
#'
#' lyt <- basic_table() %>%
#'   split_rows_by(var = "STRATA1", label_pos = "topleft") %>%
#'   split_rows_by(
#'     var = "SEX",
#'     label_pos = "topleft",
#'     child_labels = "hidden"
#'   ) %>% # Removes duplicated labels
#'   analyze_vars_in_cols(vars = "AGE")
#' result <- build_table(lyt = lyt, df = adpp)
#' result
#'
#' # By selecting just some statistics and ad-hoc labels
#' lyt <- basic_table() %>%
#'   split_rows_by(var = "ARM", label_pos = "topleft") %>%
#'   split_rows_by(
#'     var = "SEX",
#'     label_pos = "topleft",
#'     child_labels = "hidden",
#'     split_fun = drop_split_levels
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AGE",
#'     .stats = c("n", "cv", "geom_mean"),
#'     .labels = c(
#'       n = "aN",
#'       cv = "aCV",
#'       geom_mean = "aGeomMean"
#'     )
#'   )
#' result <- build_table(lyt = lyt, df = adpp)
#' result
#'
#' # Changing row labels
#' lyt <- basic_table() %>%
#'   analyze_vars_in_cols(
#'     vars = "AGE",
#'     row_labels = "some custom label"
#'   )
#' result <- build_table(lyt, df = adpp)
#' result
#'
#' # Pharmacokinetic parameters
#' lyt <- basic_table() %>%
#'   split_rows_by(
#'     var = "TLG_DISPLAY",
#'     split_label = "PK Parameter",
#'     label_pos = "topleft",
#'     child_labels = "hidden"
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AVAL"
#'   )
#' result <- build_table(lyt, df = adpp)
#' result
#'
#' # Multiple calls (summarize label and analyze underneath)
#' lyt <- basic_table() %>%
#'   split_rows_by(
#'     var = "TLG_DISPLAY",
#'     split_label = "PK Parameter",
#'     label_pos = "topleft"
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AVAL",
#'     do_summarize_row_groups = TRUE # does a summarize level
#'   ) %>%
#'   split_rows_by("SEX",
#'     child_labels = "hidden",
#'     label_pos = "topleft"
#'   ) %>%
#'   analyze_vars_in_cols(
#'     vars = "AVAL",
#'     split_col_vars = FALSE # avoids re-splitting the columns
#'   )
#' result <- build_table(lyt, df = adpp)
#' result
#'
#' @export
analyze_vars_in_cols <- function(lyt,
                                 vars,
                                 ...,
                                 .stats = c(
                                   "n",
                                   "mean",
                                   "sd",
                                   "se",
                                   "cv",
                                   "geom_cv"
                                 ),
                                 .labels = c(
                                   n = "n",
                                   mean = "Mean",
                                   sd = "SD",
                                   se = "SE",
                                   cv = "CV (%)",
                                   geom_cv = "CV % Geometric Mean"
                                 ),
                                 row_labels = NULL,
                                 do_summarize_row_groups = FALSE,
                                 split_col_vars = TRUE,
                                 imp_rule = NULL,
                                 avalcat_var = "AVALCAT1",
                                 cache = FALSE,
                                 .indent_mods = NULL,
                                 na_str = default_na_str(),
                                 nested = TRUE,
                                 .formats = NULL,
                                 .aligns = NULL) {
  extra_args <- list(...)

  checkmate::assert_string(na_str, na.ok = TRUE, null.ok = TRUE)
  checkmate::assert_character(row_labels, null.ok = TRUE)
  checkmate::assert_int(.indent_mods, null.ok = TRUE)
  checkmate::assert_flag(nested)
  checkmate::assert_flag(split_col_vars)
  checkmate::assert_flag(do_summarize_row_groups)

  # Filtering
  met_grps <- paste0("analyze_vars", c("_numeric", "_counts"))
  .stats <- get_stats(met_grps, stats_in = .stats)
  formats_v <- get_formats_from_stats(stats = .stats, formats_in = .formats)
  labels_v <- get_labels_from_stats(stats = .stats, labels_in = .labels) %>% .unlist_keep_nulls()
  if ("control" %in% names(extra_args)) labels_v <- labels_v %>% labels_use_control(extra_args[["control"]], .labels)

  # Check for vars in the case that one or more are used
  if (length(vars) == 1) {
    vars <- rep(vars, length(.stats))
  } else if (length(vars) != length(.stats)) {
    stop(
      "Analyzed variables (vars) does not have the same ",
      "number of elements of specified statistics (.stats)."
    )
  }

  if (split_col_vars) {
    # Checking there is not a previous identical column split
    clyt <- tail(clayout(lyt), 1)[[1]]

    dummy_lyt <- split_cols_by_multivar(
      lyt = basic_table(),
      vars = vars,
      varlabels = labels_v
    )

    if (any(sapply(clyt, identical, y = get_last_col_split(dummy_lyt)))) {
      stop(
        "Column split called again with the same values. ",
        "This can create many unwanted columns. Please consider adding ",
        "split_col_vars = FALSE to the last call of ",
        deparse(sys.calls()[[sys.nframe() - 1]]), "."
      )
    }

    # Main col split
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = vars,
      varlabels = labels_v
    )
  }

  env <- new.env() # create caching environment

  if (do_summarize_row_groups) {
    if (length(unique(vars)) > 1) {
      stop("When using do_summarize_row_groups only one label level var should be inserted.")
    }

    # Function list for do_summarize_row_groups. Slightly different handling of labels
    cfun_list <- Map(
      function(stat, use_cache, cache_env) {
        function(u, .spl_context, labelstr, .df_row, ...) {
          # Statistic
          var_row_val <- paste(
            gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
            paste(.spl_context$value, collapse = "_"),
            sep = "_"
          )
          if (use_cache) {
            if (is.null(cache_env[[var_row_val]])) {
              cache_env[[var_row_val]] <- s_summary(u, ...)
            }
            x_stats <- cache_env[[var_row_val]]
          } else {
            x_stats <- s_summary(u, ...)
          }

          if (is.null(imp_rule) || !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
            res <- x_stats[[stat]]
          } else {
            timept <- as.numeric(gsub(".*?([0-9\\.]+).*", "\\1", tail(.spl_context$value, 1)))
            res_imp <- imputation_rule(
              .df_row, x_stats, stat,
              imp_rule = imp_rule,
              post = grepl("Predose", tail(.spl_context$value, 1)) || timept > 0,
              avalcat_var = avalcat_var
            )
            res <- res_imp[["val"]]
            na_str <- res_imp[["na_str"]]
          }

          # Label check and replacement
          if (length(row_labels) > 1) {
            if (!(labelstr %in% names(row_labels))) {
              stop(
                "Replacing the labels in do_summarize_row_groups needs a named vector",
                "that contains the split values. In the current split variable ",
                .spl_context$split[nrow(.spl_context)],
                " the labelstr value (split value by default) ", labelstr, " is not in",
                " row_labels names: ", names(row_labels)
              )
            }
            lbl <- unlist(row_labels[labelstr])
          } else {
            lbl <- labelstr
          }

          # Cell creation
          rcell(res,
            label = lbl,
            format = formats_v[names(formats_v) == stat][[1]],
            format_na_str = na_str,
            indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
            align = .aligns
          )
        }
      },
      stat = .stats,
      use_cache = cache,
      cache_env = replicate(length(.stats), env)
    )

    # Main call to rtables
    summarize_row_groups(
      lyt = lyt,
      var = unique(vars),
      cfun = cfun_list,
      na_str = na_str,
      extra_args = extra_args
    )
  } else {
    # Function list for analyze_colvars
    afun_list <- Map(
      function(stat, use_cache, cache_env) {
        function(u, .spl_context, .df_row, ...) {
          # Main statistics
          var_row_val <- paste(
            gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
            paste(.spl_context$value, collapse = "_"),
            sep = "_"
          )
          if (use_cache) {
            if (is.null(cache_env[[var_row_val]])) cache_env[[var_row_val]] <- s_summary(u, ...)
            x_stats <- cache_env[[var_row_val]]
          } else {
            x_stats <- s_summary(u, ...)
          }

          if (is.null(imp_rule) || !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
            res <- x_stats[[stat]]
          } else {
            timept <- as.numeric(gsub(".*?([0-9\\.]+).*", "\\1", tail(.spl_context$value, 1)))
            res_imp <- imputation_rule(
              .df_row, x_stats, stat,
              imp_rule = imp_rule,
              post = grepl("Predose", tail(.spl_context$value, 1)) || timept > 0,
              avalcat_var = avalcat_var
            )
            res <- res_imp[["val"]]
            na_str <- res_imp[["na_str"]]
          }

          if (is.list(res)) {
            if (length(res) > 1) {
              stop("The analyzed column produced more than one category of results.")
            } else {
              res <- unlist(res)
            }
          }

          # Label from context
          label_from_context <- .spl_context$value[nrow(.spl_context)]

          # Label switcher
          if (is.null(row_labels)) {
            lbl <- label_from_context
          } else {
            if (length(row_labels) > 1) {
              if (!(label_from_context %in% names(row_labels))) {
                stop(
                  "Replacing the labels in do_summarize_row_groups needs a named vector",
                  "that contains the split values. In the current split variable ",
                  .spl_context$split[nrow(.spl_context)],
                  " the split value ", label_from_context, " is not in",
                  " row_labels names: ", names(row_labels)
                )
              }
              lbl <- unlist(row_labels[label_from_context])
            } else {
              lbl <- row_labels
            }
          }

          # Cell creation
          rcell(res,
            label = lbl,
            format = formats_v[names(formats_v) == stat][[1]],
            format_na_str = na_str,
            indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
            align = .aligns
          )
        }
      },
      stat = .stats,
      use_cache = cache,
      cache_env = replicate(length(.stats), env)
    )

    # Main call to rtables
    analyze_colvars(lyt,
      afun = afun_list,
      na_str = na_str,
      nested = nested,
      extra_args = extra_args
    )
  }
}

# Helper function
get_last_col_split <- function(lyt) {
  tail(tail(clayout(lyt), 1)[[1]], 1)[[1]]
}

#' Difference test for two proportions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [test_proportion_diff()] creates a layout element to test the difference between two
#' proportions. The primary analysis variable, `vars`, indicates whether a response has occurred for each record. See
#' the `method` parameter for options of methods to use to calculate the p-value. Additionally, a stratification
#' variable can be supplied via the `strata` element of the `variables` argument.
#'
#' @inheritParams argument_convention
#' @param method (`string`)\cr one of `chisq`, `cmh`, `fisher`, or `schouten`; specifies the test used
#'   to calculate the p-value.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("test_proportion_diff"), type = "sh")``
#'
#' @seealso [h_prop_diff_test]
#'
#' @name prop_diff_test
#' @order 1
NULL

#' @describeIn prop_diff_test Statistics function which tests the difference between two proportions.
#'
#' @return
#' * `s_test_proportion_diff()` returns a named `list` with a single item `pval` with an attribute `label`
#'   describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.
#'
#' @keywords internal
s_test_proportion_diff <- function(df,
                                   .var,
                                   .ref_group,
                                   .in_ref_col,
                                   variables = list(strata = NULL),
                                   method = c("chisq", "schouten", "fisher", "cmh")) {
  method <- match.arg(method)
  y <- list(pval = "")

  if (!.in_ref_col) {
    assert_df_with_variables(df, list(rsp = .var))
    assert_df_with_variables(.ref_group, list(rsp = .var))
    rsp <- factor(
      c(.ref_group[[.var]], df[[.var]]),
      levels = c("TRUE", "FALSE")
    )
    grp <- factor(
      rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
      levels = c("ref", "Not-ref")
    )

    if (!is.null(variables$strata) || method == "cmh") {
      strata <- variables$strata
      checkmate::assert_false(is.null(strata))
      strata_vars <- stats::setNames(as.list(strata), strata)
      assert_df_with_variables(df, strata_vars)
      assert_df_with_variables(.ref_group, strata_vars)
      strata <- c(interaction(.ref_group[strata]), interaction(df[strata]))
    }

    tbl <- switch(method,
      cmh = table(grp, rsp, strata),
      table(grp, rsp)
    )

    y$pval <- switch(method,
      chisq = prop_chisq(tbl),
      cmh = prop_cmh(tbl),
      fisher = prop_fisher(tbl),
      schouten = prop_schouten(tbl)
    )
  }

  y$pval <- formatters::with_label(y$pval, d_test_proportion_diff(method))
  y
}

#' Description of the difference test between two proportions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function that describes the analysis in `s_test_proportion_diff`.
#'
#' @inheritParams s_test_proportion_diff
#'
#' @return A `string` describing the test from which the p-value is derived.
#'
#' @export
d_test_proportion_diff <- function(method) {
  checkmate::assert_string(method)
  meth_part <- switch(method,
    "schouten" = "Chi-Squared Test with Schouten Correction",
    "chisq" = "Chi-Squared Test",
    "cmh" = "Cochran-Mantel-Haenszel Test",
    "fisher" = "Fisher's Exact Test",
    stop(paste(method, "does not have a description"))
  )
  paste0("p-value (", meth_part, ")")
}

#' @describeIn prop_diff_test Formatted analysis function which is used as `afun` in `test_proportion_diff()`.
#'
#' @return
#' * `a_test_proportion_diff()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_test_proportion_diff <- make_afun(
  s_test_proportion_diff,
  .formats = c(pval = "x.xxxx | (<0.0001)"),
  .indent_mods = c(pval = 1L)
)

#' @describeIn prop_diff_test Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `test_proportion_diff()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_test_proportion_diff()` to the table layout.
#'
#' @examples
#' dta <- data.frame(
#'   rsp = sample(c(TRUE, FALSE), 100, TRUE),
#'   grp = factor(rep(c("A", "B"), each = 50)),
#'   strata = factor(rep(c("V", "W", "X", "Y", "Z"), each = 20))
#' )
#'
#' # With `rtables` pipelines.
#' l <- basic_table() %>%
#'   split_cols_by(var = "grp", ref_group = "B") %>%
#'   test_proportion_diff(
#'     vars = "rsp",
#'     method = "cmh", variables = list(strata = "strata")
#'   )
#'
#' build_table(l, df = dta)
#'
#' @export
#' @order 2
test_proportion_diff <- function(lyt,
                                 vars,
                                 variables = list(strata = NULL),
                                 method = c("chisq", "schouten", "fisher", "cmh"),
                                 na_str = default_na_str(),
                                 nested = TRUE,
                                 ...,
                                 var_labels = vars,
                                 show_labels = "hidden",
                                 table_names = vars,
                                 .stats = NULL,
                                 .formats = NULL,
                                 .labels = NULL,
                                 .indent_mods = NULL) {
  extra_args <- list(variables = variables, method = method, ...)

  afun <- make_afun(
    a_test_proportion_diff,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    var_labels = var_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Helper functions to test proportion differences
#'
#' Helper functions to implement various tests on the difference between two proportions.
#'
#' @param tbl (`matrix`)\cr matrix with two groups in rows and the binary response (`TRUE`/`FALSE`) in columns.
#'
#' @return A p-value.
#'
#' @seealso [prop_diff_test()] for implementation of these helper functions.
#'
#' @name h_prop_diff_test
NULL

#' @describeIn h_prop_diff_test Performs Chi-Squared test. Internally calls [stats::prop.test()].
#'
#' @keywords internal
prop_chisq <- function(tbl) {
  checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
  tbl <- tbl[, c("TRUE", "FALSE")]
  if (any(colSums(tbl) == 0)) {
    return(1)
  }
  stats::prop.test(tbl, correct = FALSE)$p.value
}

#' @describeIn h_prop_diff_test Performs stratified Cochran-Mantel-Haenszel test. Internally calls
#'   [stats::mantelhaen.test()]. Note that strata with less than two observations are automatically discarded.
#'
#' @param ary (`array`, 3 dimensions)\cr array with two groups in rows, the binary response
#'   (`TRUE`/`FALSE`) in columns, and the strata in the third dimension.
#'
#' @keywords internal
prop_cmh <- function(ary) {
  checkmate::assert_array(ary)
  checkmate::assert_integer(c(ncol(ary), nrow(ary)), lower = 2, upper = 2)
  checkmate::assert_integer(length(dim(ary)), lower = 3, upper = 3)
  strata_sizes <- apply(ary, MARGIN = 3, sum)
  if (any(strata_sizes < 5)) {
    warning("<5 data points in some strata. CMH test may be incorrect.")
    ary <- ary[, , strata_sizes > 1]
  }

  stats::mantelhaen.test(ary, correct = FALSE)$p.value
}

#' @describeIn h_prop_diff_test Performs the Chi-Squared test with Schouten correction.
#'
#' @seealso Schouten correction is based upon \insertCite{Schouten1980-kd;textual}{tern}.
#'
#' @keywords internal
prop_schouten <- function(tbl) {
  checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
  tbl <- tbl[, c("TRUE", "FALSE")]
  if (any(colSums(tbl) == 0)) {
    return(1)
  }

  n <- sum(tbl)
  n1 <- sum(tbl[1, ])
  n2 <- sum(tbl[2, ])

  ad <- diag(tbl)
  bc <- diag(apply(tbl, 2, rev))
  ac <- tbl[, 1]
  bd <- tbl[, 2]

  t_schouten <- (n - 1) *
    (abs(prod(ad) - prod(bc)) - 0.5 * min(n1, n2))^2 /
    (n1 * n2 * sum(ac) * sum(bd))

  1 - stats::pchisq(t_schouten, df = 1)
}

#' @describeIn h_prop_diff_test Performs the Fisher's exact test. Internally calls [stats::fisher.test()].
#'
#' @keywords internal
prop_fisher <- function(tbl) {
  checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
  tbl <- tbl[, c("TRUE", "FALSE")]
  stats::fisher.test(tbl)$p.value
}

#' Stack multiple grobs
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Stack grobs as a new grob with 1 column and multiple rows layout.
#'
#' @param ... grobs.
#' @param grobs (`list` of `grob`)\cr a list of grobs.
#' @param padding (`grid::unit`)\cr unit of length 1, space between each grob.
#' @param vp (`viewport` or `NULL`)\cr a [viewport()] object (or `NULL`).
#' @param name (`string`)\cr a character identifier for the grob.
#' @param gp (`gpar`)\cr a [gpar()] object.
#'
#' @return A `grob`.
#'
#' @examples
#' library(grid)
#'
#' g1 <- circleGrob(gp = gpar(col = "blue"))
#' g2 <- circleGrob(gp = gpar(col = "red"))
#' g3 <- textGrob("TEST TEXT")
#' grid.newpage()
#' grid.draw(stack_grobs(g1, g2, g3))
#'
#' showViewport()
#'
#' grid.newpage()
#' pushViewport(viewport(layout = grid.layout(1, 2)))
#' vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2)
#' grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = "test"))
#'
#' showViewport()
#' grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)
#'
#' @export
stack_grobs <- function(...,
                        grobs = list(...),
                        padding = grid::unit(2, "line"),
                        vp = NULL,
                        gp = NULL,
                        name = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "stack_grobs()",
    details = "`tern` plotting functions no longer generate `grob` objects."
  )

  checkmate::assert_true(
    all(vapply(grobs, grid::is.grob, logical(1)))
  )

  if (length(grobs) == 1) {
    return(grobs[[1]])
  }

  n_layout <- 2 * length(grobs) - 1
  hts <- lapply(
    seq(1, n_layout),
    function(i) {
      if (i %% 2 != 0) {
        grid::unit(1, "null")
      } else {
        padding
      }
    }
  )
  hts <- do.call(grid::unit.c, hts)

  main_vp <- grid::viewport(
    layout = grid::grid.layout(nrow = n_layout, ncol = 1, heights = hts)
  )

  nested_grobs <- Map(function(g, i) {
    grid::gTree(
      children = grid::gList(g),
      vp = grid::viewport(layout.pos.row = i, layout.pos.col = 1)
    )
  }, grobs, seq_along(grobs) * 2 - 1)

  grobs_mainvp <- grid::gTree(
    children = do.call(grid::gList, nested_grobs),
    vp = main_vp
  )

  grid::gTree(
    children = grid::gList(grobs_mainvp),
    vp = vp,
    gp = gp,
    name = name
  )
}

#' Arrange multiple grobs
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Arrange grobs as a new grob with `n * m (rows * cols)` layout.
#'
#' @inheritParams stack_grobs
#' @param ncol (`integer(1)`)\cr number of columns in layout.
#' @param nrow (`integer(1)`)\cr number of rows in layout.
#' @param padding_ht (`grid::unit`)\cr unit of length 1, vertical space between each grob.
#' @param padding_wt (`grid::unit`)\cr unit of length 1, horizontal space between each grob.
#'
#' @return A `grob`.
#'
#' @examples
#' library(grid)
#'
#' \donttest{
#' num <- lapply(1:9, textGrob)
#' grid::grid.newpage()
#' grid.draw(arrange_grobs(grobs = num, ncol = 2))
#'
#' showViewport()
#'
#' g1 <- circleGrob(gp = gpar(col = "blue"))
#' g2 <- circleGrob(gp = gpar(col = "red"))
#' g3 <- textGrob("TEST TEXT")
#' grid::grid.newpage()
#' grid.draw(arrange_grobs(g1, g2, g3, nrow = 2))
#'
#' showViewport()
#'
#' grid::grid.newpage()
#' grid.draw(arrange_grobs(g1, g2, g3, ncol = 3))
#'
#' grid::grid.newpage()
#' grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2)))
#' vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2)
#' grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1))
#'
#' showViewport()
#' }
#' @export
arrange_grobs <- function(...,
                          grobs = list(...),
                          ncol = NULL, nrow = NULL,
                          padding_ht = grid::unit(2, "line"),
                          padding_wt = grid::unit(2, "line"),
                          vp = NULL,
                          gp = NULL,
                          name = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "arrange_grobs()",
    details = "`tern` plotting functions no longer generate `grob` objects."
  )

  checkmate::assert_true(
    all(vapply(grobs, grid::is.grob, logical(1)))
  )

  if (length(grobs) == 1) {
    return(grobs[[1]])
  }

  if (is.null(ncol) && is.null(nrow)) {
    ncol <- 1
    nrow <- ceiling(length(grobs) / ncol)
  } else if (!is.null(ncol) && is.null(nrow)) {
    nrow <- ceiling(length(grobs) / ncol)
  } else if (is.null(ncol) && !is.null(nrow)) {
    ncol <- ceiling(length(grobs) / nrow)
  }

  if (ncol * nrow < length(grobs)) {
    stop("specififed ncol and nrow are not enough for arranging the grobs ")
  }

  if (ncol == 1) {
    return(stack_grobs(grobs = grobs, padding = padding_ht, vp = vp, gp = gp, name = name))
  }

  n_col <- 2 * ncol - 1
  n_row <- 2 * nrow - 1
  hts <- lapply(
    seq(1, n_row),
    function(i) {
      if (i %% 2 != 0) {
        grid::unit(1, "null")
      } else {
        padding_ht
      }
    }
  )
  hts <- do.call(grid::unit.c, hts)

  wts <- lapply(
    seq(1, n_col),
    function(i) {
      if (i %% 2 != 0) {
        grid::unit(1, "null")
      } else {
        padding_wt
      }
    }
  )
  wts <- do.call(grid::unit.c, wts)

  main_vp <- grid::viewport(
    layout = grid::grid.layout(nrow = n_row, ncol = n_col, widths = wts, heights = hts)
  )

  nested_grobs <- list()
  k <- 0
  for (i in seq(nrow) * 2 - 1) {
    for (j in seq(ncol) * 2 - 1) {
      k <- k + 1
      if (k <= length(grobs)) {
        nested_grobs <- c(
          nested_grobs,
          list(grid::gTree(
            children = grid::gList(grobs[[k]]),
            vp = grid::viewport(layout.pos.row = i, layout.pos.col = j)
          ))
        )
      }
    }
  }
  grobs_mainvp <- grid::gTree(
    children = do.call(grid::gList, nested_grobs),
    vp = main_vp
  )

  grid::gTree(
    children = grid::gList(grobs_mainvp),
    vp = vp,
    gp = gp,
    name = name
  )
}

#' Draw `grob`
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Draw grob on device page.
#'
#' @param grob (`grob`)\cr grid object.
#' @param newpage (`flag`)\cr draw on a new page.
#' @param vp (`viewport` or `NULL`)\cr a [viewport()] object (or `NULL`).
#'
#' @return A `grob`.
#'
#' @examples
#' library(dplyr)
#' library(grid)
#'
#' \donttest{
#' rect <- rectGrob(width = grid::unit(0.5, "npc"), height = grid::unit(0.5, "npc"))
#' rect %>% draw_grob(vp = grid::viewport(angle = 45))
#'
#' num <- lapply(1:10, textGrob)
#' num %>%
#'   arrange_grobs(grobs = .) %>%
#'   draw_grob()
#' showViewport()
#' }
#'
#' @export
draw_grob <- function(grob, newpage = TRUE, vp = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "draw_grob()",
    details = "`tern` plotting functions no longer generate `grob` objects."
  )

  if (newpage) {
    grid::grid.newpage()
  }
  if (!is.null(vp)) {
    grid::pushViewport(vp)
  }
  grid::grid.draw(grob)
}

tern_grob <- function(x) {
  class(x) <- unique(c("ternGrob", class(x)))
  x
}

#' @keywords internal
print.ternGrob <- function(x, ...) {
  grid::grid.newpage()
  grid::grid.draw(x)
}

#' Tabulate binary response by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The [tabulate_rsp_subgroups()] function creates a layout element to tabulate binary response by subgroup, returning
#' statistics including response rate and odds ratio for each population subgroup. The table is created from `df`, a
#' list of data frames returned by [extract_rsp_subgroups()], with the statistics to include specified via the `vars`
#' parameter.
#'
#' A forest plot can be created from the resulting table using the [g_forest()] function.
#'
#' @inheritParams extract_rsp_subgroups
#' @inheritParams argument_convention
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. Tables typically used as part of forest plot.
#'
#' @seealso [extract_rsp_subgroups()]
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
#'   droplevels() %>%
#'   mutate(
#'     # Reorder levels of factor to make the placebo group the reference arm.
#'     ARM = fct_relevel(ARM, "B: Placebo"),
#'     rsp = AVALC == "CR"
#'   )
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' # Unstratified analysis.
#' df <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f
#' )
#' df
#'
#' # Stratified analysis.
#' df_strat <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strata = "STRATA1"),
#'   data = adrs_f
#' )
#' df_strat
#'
#' # Grouping of the BMRKR2 levels.
#' df_grouped <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#' df_grouped
#'
#' @name response_subgroups
#' @order 1
NULL

#' Prepare response data for population subgroups in data frames
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares response rates and odds ratios for population subgroups in data frames. Simple wrapper
#' for [h_odds_ratio_subgroups_df()] and [h_proportion_subgroups_df()]. Result is a list of two
#' `data.frames`: `prop` and `or`. `variables` corresponds to the names of variables found in `data`,
#' passed as a named `list` and requires elements `rsp`, `arm` and optionally `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams response_subgroups
#' @param label_all (`string`)\cr label for the total population analysis.
#'
#' @return A named list of two elements:
#'   * `prop`: A `data.frame` containing columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`, `var`,
#'     `var_label`, and `row_type`.
#'   * `or`: A `data.frame` containing columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`,
#'     `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @seealso [response_subgroups]
#'
#' @export
extract_rsp_subgroups <- function(variables,
                                  data,
                                  groups_lists = list(),
                                  conf_level = 0.95,
                                  method = NULL,
                                  label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_rsp_subgroups() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  df_prop <- h_proportion_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    label_all = label_all
  )
  df_or <- h_odds_ratio_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    conf_level = conf_level,
    method = method,
    label_all = label_all
  )

  list(prop = df_prop, or = df_or)
}

#' @describeIn response_subgroups Formatted analysis function which is used as `afun` in `tabulate_rsp_subgroups()`.
#'
#' @return
#' * `a_response_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_response_subgroups <- function(.formats = list(
                                   n = "xx", # nolint start
                                   n_rsp = "xx",
                                   prop = "xx.x%",
                                   n_tot = "xx",
                                   or = list(format_extreme_values(2L)),
                                   ci = list(format_extreme_values_ci(2L)),
                                   pval = "x.xxxx | (<0.0001)",
                                   riskdiff = "xx.x (xx.x - xx.x)" # nolint end
                                 ),
                                 na_str = default_na_str()) {
  checkmate::assert_list(.formats)
  checkmate::assert_subset(
    names(.formats),
    c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval", "riskdiff")
  )

  afun_lst <- Map(
    function(stat, fmt, na_str) {
      function(df, labelstr = "", ...) {
        in_rows(
          .list = as.list(df[[stat]]),
          .labels = as.character(df$subgroup),
          .formats = fmt,
          .format_na_strs = na_str
        )
      }
    },
    stat = names(.formats),
    fmt = .formats,
    na_str = na_str
  )

  afun_lst
}

#' @describeIn response_subgroups Table-creating function which creates a table
#'   summarizing binary response by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
#'   and [rtables::summarize_row_groups()].
#'
#' @param df (`list`)\cr a list of data frames containing all analysis variables. List should be
#'   created using [extract_rsp_subgroups()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n`: Total number of observations per group.
#'   * `n_rsp`: Number of responders per group.
#'   * `prop`: Proportion of responders.
#'   * `n_tot`: Total number of observations.
#'   * `or`: Odds ratio.
#'   * `ci` : Confidence interval of odds ratio.
#'   * `pval`: p-value of the effect.
#'   Note, the statistics `n_tot`, `or`, and `ci` are required.
#' @param riskdiff (`list`)\cr if a risk (proportion) difference column should be added, a list of settings to apply
#'   within the column. See [control_riskdiff()] for details. If `NULL`, no risk difference column will be added. If
#'   `riskdiff$arm_x` and `riskdiff$arm_y` are `NULL`, the first level of `df$prop$arm` will be used as `arm_x` and
#'   the second level as `arm_y`.
#'
#' @return An `rtables` table summarizing binary response by subgroup.
#'
#' @examples
#' # Table with default columns
#' basic_table() %>%
#'   tabulate_rsp_subgroups(df)
#'
#' # Table with selected columns
#' basic_table() %>%
#'   tabulate_rsp_subgroups(
#'     df = df,
#'     vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
#'   )
#'
#' # Table with risk difference column added
#' basic_table() %>%
#'   tabulate_rsp_subgroups(
#'     df,
#'     riskdiff = control_riskdiff(
#'       arm_x = levels(df$prop$arm)[1],
#'       arm_y = levels(df$prop$arm)[2]
#'     )
#'   )
#'
#' @export
#' @order 2
tabulate_rsp_subgroups <- function(lyt,
                                   df,
                                   vars = c("n_tot", "n", "prop", "or", "ci"),
                                   groups_lists = list(),
                                   label_all = "All Patients",
                                   riskdiff = NULL,
                                   na_str = default_na_str(),
                                   .formats = c(
                                     n = "xx", n_rsp = "xx", prop = "xx.x%", n_tot = "xx",
                                     or = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)),
                                     pval = "x.xxxx | (<0.0001)"
                                   )) {
  checkmate::assert_list(riskdiff, null.ok = TRUE)
  checkmate::assert_true(all(c("n_tot", "or", "ci") %in% vars))
  if ("pval" %in% vars && !"pval" %in% names(df$or)) {
    warning(
      'The "pval" statistic has been selected but is not present in "df" so it will not be included in the output ',
      'table. To include the "pval" statistic, please specify a p-value test when generating "df" via ',
      'the "method" argument to `extract_rsp_subgroups()`. If method = "cmh", strata must also be specified via the ',
      '"variables" argument to `extract_rsp_subgroups()`.'
    )
  }

  # Create "ci" column from "lcl" and "ucl"
  df$or$ci <- combine_vectors(df$or$lcl, df$or$ucl)

  # Fill in missing formats with defaults
  default_fmts <- eval(formals(tabulate_rsp_subgroups)$.formats)
  .formats <- c(.formats, default_fmts[vars[!vars %in% names(.formats)]])

  # Extract additional parameters from df
  conf_level <- df$or$conf_level[1]
  method <- if ("pval_label" %in% names(df$or)) df$or$pval_label[1] else NULL
  colvars <- d_rsp_subgroups_colvars(vars, conf_level = conf_level, method = method)
  prop_vars <- intersect(colvars$vars, c("n", "prop", "n_rsp"))
  or_vars <- intersect(names(colvars$labels), c("n_tot", "or", "ci", "pval"))
  colvars_prop <- list(vars = prop_vars, labels = colvars$labels[prop_vars])
  colvars_or <- list(vars = or_vars, labels = colvars$labels[or_vars])

  extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method, label_all = label_all)

  # Get analysis function for each statistic
  afun_lst <- a_response_subgroups(.formats = c(.formats, riskdiff = riskdiff$format), na_str = na_str)

  # Add risk difference column
  if (!is.null(riskdiff)) {
    if (is.null(riskdiff$arm_x)) riskdiff$arm_x <- levels(df$prop$arm)[1]
    if (is.null(riskdiff$arm_y)) riskdiff$arm_y <- levels(df$prop$arm)[2]
    colvars_or$vars <- c(colvars_or$vars, "riskdiff")
    colvars_or$labels <- c(colvars_or$labels, riskdiff = riskdiff$col_label)
    arm_cols <- paste(rep(c("n_rsp", "n_rsp", "n", "n")), c(riskdiff$arm_x, riskdiff$arm_y), sep = "_")

    df_prop_diff <- df$prop %>%
      dplyr::select(-"prop") %>%
      tidyr::pivot_wider(
        id_cols = c("subgroup", "var", "var_label", "row_type"),
        names_from = "arm",
        values_from = c("n", "n_rsp")
      ) %>%
      dplyr::rowwise() %>%
      dplyr::mutate(
        riskdiff = stat_propdiff_ci(
          x = as.list(.data[[arm_cols[1]]]),
          y = as.list(.data[[arm_cols[2]]]),
          N_x = .data[[arm_cols[3]]],
          N_y = .data[[arm_cols[4]]]
        )
      ) %>%
      dplyr::select(-dplyr::all_of(arm_cols))

    df$or <- df$or %>%
      dplyr::left_join(
        df_prop_diff,
        by = c("subgroup", "var", "var_label", "row_type")
      )
  }

  # Add columns from table_prop (optional)
  if (length(colvars_prop$vars) > 0) {
    lyt_prop <- split_cols_by(lyt = lyt, var = "arm")
    lyt_prop <- split_cols_by_multivar(
      lyt = lyt_prop,
      vars = colvars_prop$vars,
      varlabels = colvars_prop$labels
    )

    # Add "All Patients" row
    lyt_prop <- split_rows_by(
      lyt = lyt_prop,
      var = "row_type",
      split_fun = keep_split_levels("content"),
      nested = FALSE,
      child_labels = "hidden"
    )
    lyt_prop <- analyze_colvars(
      lyt = lyt_prop,
      afun = afun_lst[names(colvars_prop$labels)],
      na_str = na_str,
      extra_args = extra_args
    )

    # Add analysis rows
    if ("analysis" %in% df$prop$row_type) {
      lyt_prop <- split_rows_by(
        lyt = lyt_prop,
        var = "row_type",
        split_fun = keep_split_levels("analysis"),
        nested = FALSE,
        child_labels = "hidden"
      )
      lyt_prop <- split_rows_by(lyt = lyt_prop, var = "var_label", nested = TRUE)
      lyt_prop <- analyze_colvars(
        lyt = lyt_prop,
        afun = afun_lst[names(colvars_prop$labels)],
        na_str = na_str,
        inclNAs = TRUE,
        extra_args = extra_args
      )
    }

    table_prop <- build_table(lyt_prop, df = df$prop)
  } else {
    table_prop <- NULL
  }

  # Add columns from table_or ("n_tot", "or", and "ci" required)
  lyt_or <- split_cols_by(lyt = lyt, var = "arm")
  lyt_or <- split_cols_by_multivar(
    lyt = lyt_or,
    vars = colvars_or$vars,
    varlabels = colvars_or$labels
  )

  # Add "All Patients" row
  lyt_or <- split_rows_by(
    lyt = lyt_or,
    var = "row_type",
    split_fun = keep_split_levels("content"),
    nested = FALSE,
    child_labels = "hidden"
  )
  lyt_or <- analyze_colvars(
    lyt = lyt_or,
    afun = afun_lst[names(colvars_or$labels)],
    na_str = na_str,
    extra_args = extra_args
  ) %>%
    append_topleft("Baseline Risk Factors")

  # Add analysis rows
  if ("analysis" %in% df$or$row_type) {
    lyt_or <- split_rows_by(
      lyt = lyt_or,
      var = "row_type",
      split_fun = keep_split_levels("analysis"),
      nested = FALSE,
      child_labels = "hidden"
    )
    lyt_or <- split_rows_by(lyt = lyt_or, var = "var_label", nested = TRUE)
    lyt_or <- analyze_colvars(
      lyt = lyt_or,
      afun = afun_lst[names(colvars_or$labels)],
      na_str = na_str,
      inclNAs = TRUE,
      extra_args = extra_args
    )
  }

  table_or <- build_table(lyt_or, df = df$or)

  # Join tables, add forest plot attributes
  n_tot_id <- match("n_tot", colvars_or$vars)
  if (is.null(table_prop)) {
    result <- table_or
    or_id <- match("or", colvars_or$vars)
    ci_id <- match("ci", colvars_or$vars)
  } else {
    result <- cbind_rtables(table_or[, n_tot_id], table_prop, table_or[, -n_tot_id])
    or_id <- 1L + ncol(table_prop) + match("or", colvars_or$vars[-n_tot_id])
    ci_id <- 1L + ncol(table_prop) + match("ci", colvars_or$vars[-n_tot_id])
    n_tot_id <- 1L
  }
  structure(
    result,
    forest_header = paste0(levels(df$prop$arm), "\nBetter"),
    col_x = or_id,
    col_ci = ci_id,
    col_symbol_size = n_tot_id
  )
}

#' Labels for column variables in binary response by subgroup table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Internal function to check variables included in [tabulate_rsp_subgroups()] and create column labels.
#'
#' @inheritParams argument_convention
#' @inheritParams tabulate_rsp_subgroups
#'
#' @return A `list` of variables to tabulate and their labels.
#'
#' @export
d_rsp_subgroups_colvars <- function(vars,
                                    conf_level = NULL,
                                    method = NULL) {
  checkmate::assert_character(vars)
  checkmate::assert_subset(c("n_tot", "or", "ci"), vars)
  checkmate::assert_subset(
    vars,
    c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval")
  )

  varlabels <- c(
    n = "n",
    n_rsp = "Responders",
    prop = "Response (%)",
    n_tot = "Total n",
    or = "Odds Ratio"
  )
  colvars <- vars

  if ("ci" %in% colvars) {
    checkmate::assert_false(is.null(conf_level))

    varlabels <- c(
      varlabels,
      ci = paste0(100 * conf_level, "% CI")
    )

    # The `lcl`` variable is just a placeholder available in the analysis data,
    # it is not acutally used in the tabulation.
    # Variables used in the tabulation are lcl and ucl, see `a_response_subgroups` for details.
    colvars[colvars == "ci"] <- "lcl"
  }

  if ("pval" %in% colvars) {
    varlabels <- c(
      varlabels,
      pval = method
    )
  }

  list(
    vars = colvars,
    labels = varlabels[vars]
  )
}

#' Helper functions for tabulating biomarker effects on survival by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions which are documented here separately to not confuse the user
#' when reading about the user-facing functions.
#'
#' @inheritParams survival_biomarkers_subgroups
#' @inheritParams argument_convention
#' @inheritParams fit_coxreg_multivar
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte, fill = FALSE)
#'
#' adtte_f <- adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' @name h_survival_biomarkers_subgroups
NULL

#' @describeIn h_survival_biomarkers_subgroups Helps with converting the "survival" function variable list
#'   to the "Cox regression" variable list. The reason is that currently there is an inconsistency between the variable
#'   names accepted by `extract_survival_subgroups()` and `fit_coxreg_multivar()`.
#'
#' @param biomarker (`string`)\cr the name of the biomarker variable.
#'
#' @return
#' * `h_surv_to_coxreg_variables()` returns a named `list` of elements `time`, `event`, `arm`,
#'   `covariates`, and `strata`.
#'
#' @examples
#' # This is how the variable list is converted internally.
#' h_surv_to_coxreg_variables(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "EVNT",
#'     covariates = c("A", "B"),
#'     strata = "D"
#'   ),
#'   biomarker = "AGE"
#' )
#'
#' @export
h_surv_to_coxreg_variables <- function(variables, biomarker) {
  checkmate::assert_list(variables)
  checkmate::assert_string(variables$tte)
  checkmate::assert_string(variables$is_event)
  checkmate::assert_string(biomarker)
  list(
    time = variables$tte,
    event = variables$is_event,
    arm = biomarker,
    covariates = variables$covariates,
    strata = variables$strata
  )
}

#' @describeIn h_survival_biomarkers_subgroups Prepares estimates for number of events, patients and median survival
#'   times, as well as hazard ratio estimates, confidence intervals and p-values, for multiple biomarkers
#'   in a given single data set.
#'   `variables` corresponds to names of variables found in `data`, passed as a named list and requires elements
#'   `tte`, `is_event`, `biomarkers` (vector of continuous biomarker variables) and optionally `subgroups` and `strata`.
#'
#' @return
#' * `h_coxreg_mult_cont_df()` returns a `data.frame` containing estimates and statistics for the selected biomarkers.
#'
#' @examples
#' # For a single population, estimate separately the effects
#' # of two biomarkers.
#' df <- h_coxreg_mult_cont_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adtte_f
#' )
#' df
#'
#' # If the data set is empty, still the corresponding rows with missings are returned.
#' h_coxreg_mult_cont_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "REGION1",
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adtte_f[NULL, ]
#' )
#'
#' @export
h_coxreg_mult_cont_df <- function(variables,
                                  data,
                                  control = control_coxreg()) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_coxreg_mult_cont_df() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  assert_df_with_variables(data, variables)
  checkmate::assert_list(control, names = "named")
  checkmate::assert_character(variables$biomarkers, min.len = 1, any.missing = FALSE)
  conf_level <- control[["conf_level"]]
  pval_label <- paste0(
    # the regex capitalizes the first letter of the string / senetence.
    "p-value (", gsub("(^[a-z])", "\\U\\1", trimws(control[["pval_method"]]), perl = TRUE), ")"
  )
  # If there is any data, run model, otherwise return empty results.
  if (nrow(data) > 0) {
    bm_cols <- match(variables$biomarkers, names(data))
    l_result <- lapply(variables$biomarkers, function(bm) {
      coxreg_list <- fit_coxreg_multivar(
        variables = h_surv_to_coxreg_variables(variables, bm),
        data = data,
        control = control
      )
      result <- do.call(
        h_coxreg_multivar_extract,
        c(list(var = bm), coxreg_list[c("mod", "data", "control")])
      )
      data_fit <- as.data.frame(as.matrix(coxreg_list$mod$y))
      data_fit$status <- as.logical(data_fit$status)
      median <- s_surv_time(
        df = data_fit,
        .var = "time",
        is_event = "status"
      )$median
      data.frame(
        # Dummy column needed downstream to create a nested header.
        biomarker = bm,
        biomarker_label = formatters::var_labels(data[bm], fill = TRUE),
        n_tot = coxreg_list$mod$n,
        n_tot_events = coxreg_list$mod$nevent,
        median = as.numeric(median),
        result[1L, c("hr", "lcl", "ucl")],
        conf_level = conf_level,
        pval = result[1L, "pval"],
        pval_label = pval_label,
        stringsAsFactors = FALSE
      )
    })
    do.call(rbind, args = c(l_result, make.row.names = FALSE))
  } else {
    data.frame(
      biomarker = variables$biomarkers,
      biomarker_label = formatters::var_labels(data[variables$biomarkers], fill = TRUE),
      n_tot = 0L,
      n_tot_events = 0L,
      median = NA,
      hr = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      pval = NA,
      pval_label = pval_label,
      row.names = seq_along(variables$biomarkers),
      stringsAsFactors = FALSE
    )
  }
}

#' @describeIn h_survival_biomarkers_subgroups Prepares a single sub-table given a `df_sub` containing
#'   the results for a single biomarker.
#'
#' @param df (`data.frame`)\cr results for a single biomarker, as part of what is
#'   returned by [extract_survival_biomarkers()] (it needs a couple of columns which are
#'   added by that high-level function relative to what is returned by [h_coxreg_mult_cont_df()],
#'   see the example).
#'
#' @return
#' * `h_tab_surv_one_biomarker()` returns an `rtables` table object with the given statistics arranged in columns.
#'
#' @examples
#' # Starting from above `df`, zoom in on one biomarker and add required columns.
#' df1 <- df[1, ]
#' df1$subgroup <- "All patients"
#' df1$row_type <- "content"
#' df1$var <- "ALL"
#' df1$var_label <- "All patients"
#' h_tab_surv_one_biomarker(
#'   df1,
#'   vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
#'   time_unit = "days"
#' )
#'
#' @export
h_tab_surv_one_biomarker <- function(df,
                                     vars,
                                     time_unit,
                                     na_str = default_na_str(),
                                     .indent_mods = 0L,
                                     ...) {
  afuns <- a_survival_subgroups(na_str = na_str)[vars]
  colvars <- d_survival_subgroups_colvars(
    vars,
    conf_level = df$conf_level[1],
    method = df$pval_label[1],
    time_unit = time_unit
  )
  h_tab_one_biomarker(
    df = df,
    afuns = afuns,
    colvars = colvars,
    na_str = na_str,
    .indent_mods = .indent_mods,
    ...
  )
}

#' Formatting functions
#'
#' See below for the list of formatting functions created in `tern` to work with `rtables`.
#'
#' Other available formats can be listed via [`formatters::list_valid_format_labels()`]. Additional
#' custom formats can be created via the [`formatters::sprintf_format()`] function.
#'
#' @family formatting functions
#' @name formatting_functions
NULL

#' Format fraction and percentage
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a fraction together with ratio in percent.
#'
#' @param x (named `integer`)\cr vector with elements `num` and `denom`.
#' @param ... not used. Required for `rtables` interface.
#'
#' @return A string in the format `num / denom (ratio %)`. If `num` is 0, the format is `num / denom`.
#'
#' @examples
#' format_fraction(x = c(num = 2L, denom = 3L))
#' format_fraction(x = c(num = 0L, denom = 3L))
#'
#' @family formatting functions
#' @export
format_fraction <- function(x, ...) {
  attr(x, "label") <- NULL

  checkmate::assert_vector(x)
  checkmate::assert_count(x["num"])
  checkmate::assert_count(x["denom"])

  result <- if (x["num"] == 0) {
    paste0(x["num"], "/", x["denom"])
  } else {
    paste0(
      x["num"], "/", x["denom"],
      " (", round(x["num"] / x["denom"] * 100, 1), "%)"
    )
  }

  return(result)
}

#' Format fraction and percentage with fixed single decimal place
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a fraction together with ratio in percent with fixed single decimal place.
#' Includes trailing zero in case of whole number percentages to always keep one decimal place.
#'
#' @inheritParams format_fraction
#'
#' @return A string in the format `num / denom (ratio %)`. If `num` is 0, the format is `num / denom`.
#'
#' @examples
#' format_fraction_fixed_dp(x = c(num = 1L, denom = 2L))
#' format_fraction_fixed_dp(x = c(num = 1L, denom = 4L))
#' format_fraction_fixed_dp(x = c(num = 0L, denom = 3L))
#'
#' @family formatting functions
#' @export
format_fraction_fixed_dp <- function(x, ...) {
  attr(x, "label") <- NULL
  checkmate::assert_vector(x)
  checkmate::assert_count(x["num"])
  checkmate::assert_count(x["denom"])

  result <- if (x["num"] == 0) {
    paste0(x["num"], "/", x["denom"])
  } else {
    paste0(
      x["num"], "/", x["denom"],
      " (", sprintf("%.1f", round(x["num"] / x["denom"] * 100, 1)), "%)"
    )
  }
  return(result)
}

#' Format count and fraction
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a count together with fraction with special consideration when count is `0`.
#'
#' @param x (`numeric(2)`)\cr vector of length 2 with count and fraction, respectively.
#' @param ... not used. Required for `rtables` interface.
#'
#' @return A string in the format `count (fraction %)`. If `count` is 0, the format is `0`.
#'
#' @examples
#' format_count_fraction(x = c(2, 0.6667))
#' format_count_fraction(x = c(0, 0))
#'
#' @family formatting functions
#' @export
format_count_fraction <- function(x, ...) {
  attr(x, "label") <- NULL

  if (any(is.na(x))) {
    return("NA")
  }

  checkmate::assert_vector(x)
  checkmate::assert_integerish(x[1])
  assert_proportion_value(x[2], include_boundaries = TRUE)

  result <- if (x[1] == 0) {
    "0"
  } else {
    paste0(x[1], " (", round(x[2] * 100, 1), "%)")
  }

  return(result)
}

#' Format count and percentage with fixed single decimal place
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Formats a count together with fraction with special consideration when count is `0`.
#'
#' @inheritParams format_count_fraction
#'
#' @return A string in the format `count (fraction %)`. If `count` is 0, the format is `0`.
#'
#' @examples
#' format_count_fraction_fixed_dp(x = c(2, 0.6667))
#' format_count_fraction_fixed_dp(x = c(2, 0.5))
#' format_count_fraction_fixed_dp(x = c(0, 0))
#'
#' @family formatting functions
#' @export
format_count_fraction_fixed_dp <- function(x, ...) {
  attr(x, "label") <- NULL

  if (any(is.na(x))) {
    return("NA")
  }

  checkmate::assert_vector(x)
  checkmate::assert_integerish(x[1])
  assert_proportion_value(x[2], include_boundaries = TRUE)

  result <- if (x[1] == 0) {
    "0"
  } else if (.is_equal_float(x[2], 1)) {
    sprintf("%d (100%%)", x[1])
  } else {
    sprintf("%d (%.1f%%)", x[1], x[2] * 100)
  }

  return(result)
}

#' Format count and fraction with special case for count < 10
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a count together with fraction with special consideration when count is less than 10.
#'
#' @inheritParams format_count_fraction
#'
#' @return A string in the format `count (fraction %)`. If `count` is less than 10, only `count` is printed.
#'
#' @examples
#' format_count_fraction_lt10(x = c(275, 0.9673))
#' format_count_fraction_lt10(x = c(2, 0.6667))
#' format_count_fraction_lt10(x = c(9, 1))
#'
#' @family formatting functions
#' @export
format_count_fraction_lt10 <- function(x, ...) {
  attr(x, "label") <- NULL

  if (any(is.na(x))) {
    return("NA")
  }

  checkmate::assert_vector(x)
  checkmate::assert_integerish(x[1])
  assert_proportion_value(x[2], include_boundaries = TRUE)

  result <- if (x[1] < 10) {
    paste0(x[1])
  } else {
    paste0(x[1], " (", round(x[2] * 100, 1), "%)")
  }

  return(result)
}

#' Format XX as a formatting function
#'
#' Translate a string where x and dots are interpreted as number place
#' holders, and others as formatting elements.
#'
#' @param str (`string`)\cr template.
#'
#' @return An `rtables` formatting function.
#'
#' @examples
#' test <- list(c(1.658, 0.5761), c(1e1, 785.6))
#'
#' z <- format_xx("xx (xx.x)")
#' sapply(test, z)
#'
#' z <- format_xx("xx.x - xx.x")
#' sapply(test, z)
#'
#' z <- format_xx("xx.x, incl. xx.x% NE")
#' sapply(test, z)
#'
#' @family formatting functions
#' @export
format_xx <- function(str) {
  # Find position in the string.
  positions <- gregexpr(pattern = "x+\\.x+|x+", text = str, perl = TRUE)
  x_positions <- regmatches(x = str, m = positions)[[1]]

  # Roundings depends on the number of x behind [.].
  roundings <- lapply(
    X = x_positions,
    function(x) {
      y <- strsplit(split = "\\.", x = x)[[1]]
      rounding <- function(x) {
        round(x, digits = ifelse(length(y) > 1, nchar(y[2]), 0))
      }
      return(rounding)
    }
  )

  rtable_format <- function(x, output) {
    values <- Map(y = x, fun = roundings, function(y, fun) fun(y))
    regmatches(x = str, m = positions)[[1]] <- values
    return(str)
  }

  return(rtable_format)
}

#' Format numeric values by significant figures
#'
#' Format numeric values to print with a specified number of significant figures.
#'
#' @param sigfig (`integer(1)`)\cr number of significant figures to display.
#' @param format (`string`)\cr the format label (string) to apply when printing the value. Decimal
#'   places in string are ignored in favor of formatting by significant figures. Formats options are:
#'   `"xx"`, `"xx / xx"`, `"(xx, xx)"`, `"xx - xx"`, and `"xx (xx)"`.
#' @param num_fmt (`string`)\cr numeric format modifiers to apply to the value. Defaults to `"fg"` for
#'   standard significant figures formatting - fixed (non-scientific notation) format (`"f"`)
#'   and `sigfig` equal to number of significant figures instead of decimal places (`"g"`). See the
#'   [formatC()] `format` argument for more options.
#'
#' @return An `rtables` formatting function.
#'
#' @examples
#' fmt_3sf <- format_sigfig(3)
#' fmt_3sf(1.658)
#' fmt_3sf(1e1)
#'
#' fmt_5sf <- format_sigfig(5)
#' fmt_5sf(0.57)
#' fmt_5sf(0.000025645)
#'
#' @family formatting functions
#' @export
format_sigfig <- function(sigfig, format = "xx", num_fmt = "fg") {
  checkmate::assert_integerish(sigfig)
  format <- gsub("xx\\.|xx\\.x+", "xx", format)
  checkmate::assert_choice(format, c("xx", "xx / xx", "(xx, xx)", "xx - xx", "xx (xx)"))
  function(x, ...) {
    if (!is.numeric(x)) stop("`format_sigfig` cannot be used for non-numeric values. Please choose another format.")
    num <- formatC(signif(x, digits = sigfig), digits = sigfig, format = num_fmt, flag = "#")
    num <- gsub("\\.$", "", num) # remove trailing "."

    format_value(num, format)
  }
}

#' Format fraction with lower threshold
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formats a fraction when the second element of the input `x` is the fraction. It applies
#' a lower threshold, below which it is just stated that the fraction is smaller than that.
#'
#' @param threshold (`proportion`)\cr lower threshold.
#'
#' @return An `rtables` formatting function that takes numeric input `x` where the second
#'   element is the fraction that is formatted. If the fraction is above or equal to the threshold,
#'   then it is displayed in percentage. If it is positive but below the threshold, it returns,
#'   e.g. "<1" if the threshold is `0.01`. If it is zero, then just "0" is returned.
#'
#' @examples
#' format_fun <- format_fraction_threshold(0.05)
#' format_fun(x = c(20, 0.1))
#' format_fun(x = c(2, 0.01))
#' format_fun(x = c(0, 0))
#'
#' @family formatting functions
#' @export
format_fraction_threshold <- function(threshold) {
  assert_proportion_value(threshold)
  string_below_threshold <- paste0("<", round(threshold * 100))
  function(x, ...) {
    assert_proportion_value(x[2], include_boundaries = TRUE)
    ifelse(
      x[2] > 0.01,
      round(x[2] * 100),
      ifelse(
        x[2] == 0,
        "0",
        string_below_threshold
      )
    )
  }
}

#' Format extreme values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' `rtables` formatting functions that handle extreme values.
#'
#' @param digits (`integer(1)`)\cr number of decimal places to display.
#'
#' @details For each input, apply a format to the specified number of `digits`. If the value is
#'    below a threshold, it returns "<0.01" e.g. if the number of `digits` is 2. If the value is
#'    above a threshold, it returns ">999.99" e.g. if the number of `digits` is 2.
#'    If it is zero, then returns "0.00".
#'
#' @family formatting functions
#' @name extreme_format
NULL

#' @describeIn extreme_format Internal helper function to calculate the threshold and create formatted strings
#'  used in Formatting Functions. Returns a list with elements `threshold` and `format_string`.
#'
#' @return
#' * `h_get_format_threshold()` returns a `list` of 2 elements: `threshold`, with `low` and `high` thresholds,
#'   and `format_string`, with thresholds formatted as strings.
#'
#' @examples
#' h_get_format_threshold(2L)
#'
#' @export
h_get_format_threshold <- function(digits = 2L) {
  checkmate::assert_integerish(digits)

  low_threshold <- 1 / (10 ^ digits) # styler: off
  high_threshold <- 1000 - (1 / (10 ^ digits)) # styler: off

  string_below_threshold <- paste0("<", low_threshold)
  string_above_threshold <- paste0(">", high_threshold)

  list(
    "threshold" = c(low = low_threshold, high = high_threshold),
    "format_string" = c(low = string_below_threshold, high = string_above_threshold)
  )
}

#' @describeIn extreme_format Internal helper function to apply a threshold format to a value.
#'   Creates a formatted string to be used in Formatting Functions.
#'
#' @param x (`numeric(1)`)\cr value to format.
#'
#' @return
#' * `h_format_threshold()` returns the given value, or if the value is not within the digit threshold the relation
#'   of the given value to the digit threshold, as a formatted string.
#'
#' @examples
#' h_format_threshold(0.001)
#' h_format_threshold(1000)
#'
#' @export
h_format_threshold <- function(x, digits = 2L) {
  if (is.na(x)) {
    return(x)
  }

  checkmate::assert_numeric(x, lower = 0)

  l_fmt <- h_get_format_threshold(digits)

  result <- if (x < l_fmt$threshold["low"] && 0 < x) {
    l_fmt$format_string["low"]
  } else if (x > l_fmt$threshold["high"]) {
    l_fmt$format_string["high"]
  } else {
    sprintf(fmt = paste0("%.", digits, "f"), x)
  }

  unname(result)
}

#' Format a single extreme value
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Create a formatting function for a single extreme value.
#'
#' @inheritParams extreme_format
#'
#' @return An `rtables` formatting function that uses threshold `digits` to return a formatted extreme value.
#'
#' @examples
#' format_fun <- format_extreme_values(2L)
#' format_fun(x = 0.127)
#' format_fun(x = Inf)
#' format_fun(x = 0)
#' format_fun(x = 0.009)
#'
#' @family formatting functions
#' @export
format_extreme_values <- function(digits = 2L) {
  function(x, ...) {
    checkmate::assert_scalar(x, na.ok = TRUE)

    h_format_threshold(x = x, digits = digits)
  }
}

#' Format extreme values part of a confidence interval
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formatting Function for extreme values part of a confidence interval. Values
#' are formatted as e.g. "(xx.xx, xx.xx)" if the number of `digits` is 2.
#'
#' @inheritParams extreme_format
#'
#' @return An `rtables` formatting function that uses threshold `digits` to return a formatted extreme
#'   values confidence interval.
#'
#' @examples
#' format_fun <- format_extreme_values_ci(2L)
#' format_fun(x = c(0.127, Inf))
#' format_fun(x = c(0, 0.009))
#'
#' @family formatting functions
#' @export
format_extreme_values_ci <- function(digits = 2L) {
  function(x, ...) {
    checkmate::assert_vector(x, len = 2)
    l_result <- h_format_threshold(x = x[1], digits = digits)
    h_result <- h_format_threshold(x = x[2], digits = digits)

    paste0("(", l_result, ", ", h_result, ")")
  }
}

#' Format automatically using data significant digits
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Formatting function for the majority of default methods used in [analyze_vars()].
#' For non-derived values, the significant digits of data is used (e.g. range), while derived
#' values have one more digits (measure of location and dispersion like mean, standard deviation).
#' This function can be called internally with "auto" like, for example,
#' `.formats = c("mean" = "auto")`. See details to see how this works with the inner function.
#'
#' @param dt_var (`numeric`)\cr variable data the statistics were calculated from. Used only to
#'   find significant digits. In [analyze_vars] this comes from `.df_row` (see
#'   [rtables::additional_fun_params]), and it is the row data after the above row splits. No
#'   column split is considered.
#' @param x_stat (`string`)\cr string indicating the current statistical method used.
#'
#' @return A string that `rtables` prints in a table cell.
#'
#' @details
#' The internal function is needed to work with `rtables` default structure for
#' format functions, i.e. `function(x, ...)`, where is x are results from statistical evaluation.
#' It can be more than one element (e.g. for `.stats = "mean_sd"`).
#'
#' @examples
#' x_todo <- c(0.001, 0.2, 0.0011000, 3, 4)
#' res <- c(mean(x_todo[1:3]), sd(x_todo[1:3]))
#'
#' # x is the result coming into the formatting function -> res!!
#' format_auto(dt_var = x_todo, x_stat = "mean_sd")(x = res)
#' format_auto(x_todo, "range")(x = range(x_todo))
#' no_sc_x <- c(0.0000001, 1)
#' format_auto(no_sc_x, "range")(x = no_sc_x)
#'
#' @family formatting functions
#' @export
format_auto <- function(dt_var, x_stat) {
  function(x = "", ...) {
    checkmate::assert_numeric(x, min.len = 1)
    checkmate::assert_numeric(dt_var, min.len = 1)
    # Defaults - they may be a param in the future
    der_stats <- c(
      "mean", "sd", "se", "median", "geom_mean", "quantiles", "iqr",
      "mean_sd", "mean_se", "mean_se", "mean_ci", "mean_sei", "mean_sdi",
      "median_ci"
    )
    nonder_stats <- c("n", "range", "min", "max")

    # Safenet for miss-modifications
    stopifnot(length(intersect(der_stats, nonder_stats)) == 0) # nolint
    checkmate::assert_choice(x_stat, c(der_stats, nonder_stats))

    # Finds the max number of digits in data
    detect_dig <- vapply(dt_var, count_decimalplaces, FUN.VALUE = numeric(1)) %>%
      max()

    if (x_stat %in% der_stats) {
      detect_dig <- detect_dig + 1
    }

    # Render input
    str_vals <- formatC(x, digits = detect_dig, format = "f")
    def_fmt <- get_formats_from_stats(x_stat)[[x_stat]]
    str_fmt <- str_extract(def_fmt, invert = FALSE)[[1]]
    if (length(str_fmt) != length(str_vals)) {
      stop(
        "Number of inserted values as result (", length(str_vals),
        ") is not the same as there should be in the default tern formats for ",
        x_stat, " (-> ", def_fmt, " needs ", length(str_fmt), " values). ",
        "See tern_default_formats to check all of them."
      )
    }

    # Squashing them together
    inv_str_fmt <- str_extract(def_fmt, invert = TRUE)[[1]]
    stopifnot(length(inv_str_fmt) == length(str_vals) + 1) # nolint

    out <- vector("character", length = length(inv_str_fmt) + length(str_vals))
    is_even <- seq_along(out) %% 2 == 0
    out[is_even] <- str_vals
    out[!is_even] <- inv_str_fmt

    return(paste0(out, collapse = ""))
  }
}

# Utility function that could be useful in general
str_extract <- function(string, pattern = "xx|xx\\.|xx\\.x+", invert = FALSE) {
  regmatches(string, gregexpr(pattern, string), invert = invert)
}

# Helper function
count_decimalplaces <- function(dec) {
  if (is.na(dec)) {
    return(0)
  } else if (abs(dec - round(dec)) > .Machine$double.eps^0.5) { # For precision
    nchar(strsplit(format(dec, scientific = FALSE, trim = FALSE), ".", fixed = TRUE)[[1]][[2]])
  } else {
    return(0)
  }
}

#' Apply automatic formatting
#'
#' Checks if any of the listed formats in `.formats` are `"auto"`, and replaces `"auto"` with
#' the correct implementation of `format_auto` for the given statistics, data, and variable.
#'
#' @inheritParams argument_convention
#' @param x_stats (named `list`)\cr a named list of statistics where each element corresponds
#'   to an element in `.formats`, with matching names.
#'
#' @keywords internal
apply_auto_formatting <- function(.formats, x_stats, .df_row, .var) {
  is_auto_fmt <- vapply(.formats, function(ii) is.character(ii) && ii == "auto", logical(1))
  if (any(is_auto_fmt)) {
    auto_stats <- x_stats[is_auto_fmt]
    var_df <- .df_row[[.var]] # xxx this can be extended for the WHOLE data or single facets
    .formats[is_auto_fmt] <- lapply(names(auto_stats), format_auto, dt_var = var_df)
  }
  .formats
}

#' Count occurrences
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_occurrences()] creates a layout element to calculate occurrence counts for patients.
#'
#' This function analyzes the variable(s) supplied to `vars` and returns a table of occurrence counts for
#' each unique value (or level) of the variable(s). This variable (or variables) must be
#' non-numeric. The `id` variable is used to indicate unique subject identifiers (defaults to `USUBJID`).
#'
#' If there are multiple occurrences of the same value recorded for a patient, the value is only counted once.
#'
#' The summarize function [summarize_occurrences()] performs the same function as [count_occurrences()] except it
#' creates content rows, not data rows, to summarize the current table row/column context and operates on the level of
#' the latest row split or the root of the table if no row splits have occurred.
#'
#' @inheritParams argument_convention
#' @param drop (`flag`)\cr whether non-appearing occurrence levels should be dropped from the resulting table.
#'   Note that in that case the remaining occurrence levels in the table are sorted alphabetically.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_occurrences"), type = "sh")``
#'
#' @note By default, occurrences which don't appear in a given row split are dropped from the table and
#'   the occurrences in the table are sorted alphabetically per row split. Therefore, the corresponding layout
#'   needs to use `split_fun = drop_split_levels` in the `split_rows_by` calls. Use `drop = FALSE` if you would
#'   like to show all occurrences.
#'
#' @examples
#' library(dplyr)
#' df <- data.frame(
#'   USUBJID = as.character(c(
#'     1, 1, 2, 4, 4, 4,
#'     6, 6, 6, 7, 7, 8
#'   )),
#'   MHDECOD = c(
#'     "MH1", "MH2", "MH1", "MH1", "MH1", "MH3",
#'     "MH2", "MH2", "MH3", "MH1", "MH2", "MH4"
#'   ),
#'   ARM = rep(c("A", "B"), each = 6),
#'   SEX = c("F", "F", "M", "M", "M", "M", "F", "F", "F", "M", "M", "F")
#' )
#' df_adsl <- df %>%
#'   select(USUBJID, ARM) %>%
#'   unique()
#'
#' @name count_occurrences
#' @order 1
NULL

#' @describeIn count_occurrences Statistics function which counts number of patients that report an
#' occurrence.
#'
#' @param denom (`string`)\cr choice of denominator for proportion. Options are:
#'   * `N_col`: total number of patients in this column across rows.
#'   * `n`: number of patients with any occurrences.
#'   * `N_row`: total number of patients in this row across columns.
#'
#' @return
#' * `s_count_occurrences()` returns a list with:
#'   * `count`: list of counts with one element per occurrence.
#'   * `count_fraction`: list of counts and fractions with one element per occurrence.
#'   * `fraction`: list of numerators and denominators with one element per occurrence.
#'
#' @examples
#' # Count unique occurrences per subject.
#' s_count_occurrences(
#'   df,
#'   .N_col = 4L,
#'   .N_row = 4L,
#'   .df_row = df,
#'   .var = "MHDECOD",
#'   id = "USUBJID"
#' )
#'
#' @export
s_count_occurrences <- function(df,
                                denom = c("N_col", "n", "N_row"),
                                .N_col, # nolint
                                .N_row, # nolint
                                .df_row,
                                drop = TRUE,
                                .var = "MHDECOD",
                                id = "USUBJID") {
  checkmate::assert_flag(drop)
  assert_df_with_variables(df, list(range = .var, id = id))
  checkmate::assert_count(.N_col)
  checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[id]], classes = c("factor", "character"))

  occurrences <- if (drop) {
    # Note that we don't try to preserve original level order here since a) that would required
    # more time to look up in large original levels and b) that would fail for character input variable.
    occurrence_levels <- sort(unique(.df_row[[.var]]))
    if (length(occurrence_levels) == 0) {
      stop(
        "no empty `.df_row` input allowed when `drop = TRUE`,",
        " please use `split_fun = drop_split_levels` in the `rtables` `split_rows_by` calls"
      )
    }
    factor(df[[.var]], levels = occurrence_levels)
  } else {
    df[[.var]]
  }
  ids <- factor(df[[id]])
  denom <- match.arg(denom) %>%
    switch(
      n = nlevels(ids),
      N_row = .N_row,
      N_col = .N_col
    )
  has_occurrence_per_id <- table(occurrences, ids) > 0
  n_ids_per_occurrence <- as.list(rowSums(has_occurrence_per_id))
  cur_count_fraction <- lapply(
    n_ids_per_occurrence,
    function(i, denom) {
      if (i == 0 && denom == 0) {
        c(0, 0)
      } else {
        c(i, i / denom)
      }
    },
    denom = denom
  )

  list(
    count = n_ids_per_occurrence,
    count_fraction = cur_count_fraction,
    count_fraction_fixed_dp = cur_count_fraction,
    fraction = lapply(
      n_ids_per_occurrence,
      function(i, denom) c("num" = i, "denom" = denom),
      denom = denom
    )
  )
}

#' @describeIn count_occurrences Formatted analysis function which is used as `afun`
#'   in `count_occurrences()`.
#'
#' @return
#' * `a_count_occurrences()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_occurrences(
#'   df,
#'   .N_col = 4L,
#'   .df_row = df,
#'   .var = "MHDECOD",
#'   id = "USUBJID"
#' )
#'
#' @export
a_count_occurrences <- function(df,
                                labelstr = "",
                                id = "USUBJID",
                                denom = c("N_col", "n", "N_row"),
                                drop = TRUE,
                                .N_col, # nolint
                                .N_row, # nolint
                                .var = NULL,
                                .df_row = NULL,
                                .stats = NULL,
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL,
                                na_str = default_na_str()) {
  denom <- match.arg(denom)
  x_stats <- s_count_occurrences(
    df = df, denom = denom, .N_col = .N_col, .N_row = .N_row, .df_row = .df_row, drop = drop, .var = .var, id = id
  )
  if (is.null(unlist(x_stats))) {
    return(NULL)
  }

  # Fill in with formatting defaults if needed
  .stats <- get_stats("count_occurrences", stats_in = .stats)
  x_stats <- x_stats[.stats]
  levels_per_stats <- lapply(x_stats, names)
  .formats <- get_formats_from_stats(.stats, .formats, levels_per_stats)
  .labels <- get_labels_from_stats(.stats, .labels, levels_per_stats)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods, levels_per_stats)

  # Unlist stats
  x_stats <- x_stats %>% .unlist_keep_nulls()

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = .labels %>% .unlist_keep_nulls(),
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls(),
    .format_na_strs = na_str
  )
}

#' @describeIn count_occurrences Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_occurrences()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_occurrences()` to the table layout.
#'
#' @examples
#' # Create table layout
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_occurrences(vars = "MHDECOD", .stats = c("count_fraction"))
#'
#' # Apply table layout to data and produce `rtable` object
#' tbl <- lyt %>%
#'   build_table(df, alt_counts_df = df_adsl) %>%
#'   prune_table()
#'
#' tbl
#'
#' @export
#' @order 2
count_occurrences <- function(lyt,
                              vars,
                              id = "USUBJID",
                              drop = TRUE,
                              var_labels = vars,
                              show_labels = "hidden",
                              riskdiff = FALSE,
                              na_str = default_na_str(),
                              nested = TRUE,
                              ...,
                              table_names = vars,
                              .stats = "count_fraction_fixed_dp",
                              .formats = NULL,
                              .labels = NULL,
                              .indent_mods = NULL) {
  checkmate::assert_flag(riskdiff)

  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(id = id, drop = drop, ...)

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_occurrences" = a_count_occurrences),
        s_args = s_args
      )
    )
  }

  analyze(
    lyt = lyt,
    vars = vars,
    afun = ifelse(isFALSE(riskdiff), a_count_occurrences, afun_riskdiff),
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' @describeIn count_occurrences Layout-creating function which can take content function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_occurrences()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
#'   containing the statistics from `s_count_occurrences()` to the table layout.
#'
#' @examples
#' # Layout creating function with custom format.
#' basic_table() %>%
#'   add_colcounts() %>%
#'   split_rows_by("SEX", child_labels = "visible") %>%
#'   summarize_occurrences(
#'     var = "MHDECOD",
#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' @export
#' @order 3
summarize_occurrences <- function(lyt,
                                  var,
                                  id = "USUBJID",
                                  drop = TRUE,
                                  riskdiff = FALSE,
                                  na_str = default_na_str(),
                                  ...,
                                  .stats = "count_fraction_fixed_dp",
                                  .formats = NULL,
                                  .indent_mods = NULL,
                                  .labels = NULL) {
  checkmate::assert_flag(riskdiff)

  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(id = id, drop = drop, ...)

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_occurrences" = a_count_occurrences),
        s_args = s_args
      )
    )
  }

  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = ifelse(isFALSE(riskdiff), a_count_occurrences, afun_riskdiff),
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Individual patient plots
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Line plot(s) displaying trend in patients' parameter values over time is rendered.
#' Patients' individual baseline values can be added to the plot(s) as reference.
#'
#' @inheritParams argument_convention
#' @param xvar (`string`)\cr time point variable to be plotted on x-axis.
#' @param yvar (`string`)\cr continuous analysis variable to be plotted on y-axis.
#' @param xlab (`string`)\cr plot label for x-axis.
#' @param ylab (`string`)\cr plot label for y-axis.
#' @param id_var (`string`)\cr variable used as patient identifier.
#' @param title (`string`)\cr title for plot.
#' @param subtitle (`string`)\cr subtitle for plot.
#' @param add_baseline_hline (`flag`)\cr adds horizontal line at baseline y-value on
#'   plot when `TRUE`.
#' @param yvar_baseline (`string`)\cr variable with baseline values only.
#'   Ignored when `add_baseline_hline` is `FALSE`.
#' @param ggtheme (`theme`)\cr optional graphical theme function as provided
#'   by `ggplot2` to control outlook of plot. Use `ggplot2::theme()` to tweak the display.
#' @param plotting_choices (`string`)\cr specifies options for displaying
#'   plots. Must be one of `"all_in_one"`, `"split_by_max_obs"`, or `"separate_by_obs"`.
#' @param max_obs_per_plot (`integer(1)`)\cr number of observations to be plotted on one
#'   plot. Ignored if `plotting_choices` is not `"separate_by_obs"`.
#' @param caption (`string`)\cr optional caption below the plot.
#' @param col (`character`)\cr line colors.
#'
#' @seealso Relevant helper function [h_g_ipp()].
#'
#' @name g_ipp
#' @aliases individual_patient_plot
NULL

#' Helper function to create simple line plot over time
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Function that generates a simple line plot displaying parameter trends over time.
#'
#' @inheritParams argument_convention
#' @inheritParams g_ipp
#'
#' @return A `ggplot` line plot.
#'
#' @seealso [g_ipp()] which uses this function.
#'
#' @examples
#' library(dplyr)
#'
#' # Select a small sample of data to plot.
#' adlb <- tern_ex_adlb %>%
#'   filter(PARAMCD == "ALT", !(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
#'   slice(1:36)
#'
#' p <- h_g_ipp(
#'   df = adlb,
#'   xvar = "AVISIT",
#'   yvar = "AVAL",
#'   xlab = "Visit",
#'   id_var = "USUBJID",
#'   ylab = "SGOT/ALT (U/L)",
#'   add_baseline_hline = TRUE
#' )
#' p
#'
#' @export
h_g_ipp <- function(df,
                    xvar,
                    yvar,
                    xlab,
                    ylab,
                    id_var,
                    title = "Individual Patient Plots",
                    subtitle = "",
                    caption = NULL,
                    add_baseline_hline = FALSE,
                    yvar_baseline = "BASE",
                    ggtheme = nestcolor::theme_nest(),
                    col = NULL) {
  checkmate::assert_string(xvar)
  checkmate::assert_string(yvar)
  checkmate::assert_string(yvar_baseline)
  checkmate::assert_string(id_var)
  checkmate::assert_string(xlab)
  checkmate::assert_string(ylab)
  checkmate::assert_string(title)
  checkmate::assert_string(subtitle)
  checkmate::assert_subset(c(xvar, yvar, yvar_baseline, id_var), colnames(df))
  checkmate::assert_data_frame(df)
  checkmate::assert_flag(add_baseline_hline)
  checkmate::assert_character(col, null.ok = TRUE)

  p <- ggplot2::ggplot(
    data = df,
    mapping = ggplot2::aes(
      x = .data[[xvar]],
      y = .data[[yvar]],
      group = .data[[id_var]],
      colour = .data[[id_var]]
    )
  ) +
    ggplot2::geom_line(linewidth = 0.4) +
    ggplot2::geom_point(size = 2) +
    ggplot2::labs(
      x = xlab,
      y = ylab,
      title = title,
      subtitle = subtitle,
      caption = caption
    ) +
    ggtheme

  if (add_baseline_hline) {
    baseline_df <- df[, c(id_var, yvar_baseline)]
    baseline_df <- unique(baseline_df)

    p <- p +
      ggplot2::geom_hline(
        data = baseline_df,
        mapping = ggplot2::aes(
          yintercept = .data[[yvar_baseline]],
          colour = .data[[id_var]]
        ),
        linetype = "dotdash",
        linewidth = 0.4
      ) +
      ggplot2::geom_text(
        data = baseline_df,
        mapping = ggplot2::aes(
          x = 1,
          y = .data[[yvar_baseline]],
          label = .data[[id_var]],
          colour = .data[[id_var]]
        ),
        nudge_y = 0.025 * (max(df[, yvar], na.rm = TRUE) - min(df[, yvar], na.rm = TRUE)),
        vjust = "right",
        size = 2
      )

    if (!is.null(col)) {
      p <- p +
        ggplot2::scale_color_manual(values = col)
    }
  }
  p
}

#' @describeIn g_ipp Plotting function for individual patient plots which, depending on user
#'   preference, renders a single graphic or compiles a list of graphics that show trends in individual's parameter
#'   values over time.
#'
#' @return A `ggplot` object or a list of `ggplot` objects.
#'
#' @examples
#' library(dplyr)
#'
#' # Select a small sample of data to plot.
#' adlb <- tern_ex_adlb %>%
#'   filter(PARAMCD == "ALT", !(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
#'   slice(1:36)
#'
#' plot_list <- g_ipp(
#'   df = adlb,
#'   xvar = "AVISIT",
#'   yvar = "AVAL",
#'   xlab = "Visit",
#'   ylab = "SGOT/ALT (U/L)",
#'   title = "Individual Patient Plots",
#'   add_baseline_hline = TRUE,
#'   plotting_choices = "split_by_max_obs",
#'   max_obs_per_plot = 5
#' )
#' plot_list
#'
#' @export
g_ipp <- function(df,
                  xvar,
                  yvar,
                  xlab,
                  ylab,
                  id_var = "USUBJID",
                  title = "Individual Patient Plots",
                  subtitle = "",
                  caption = NULL,
                  add_baseline_hline = FALSE,
                  yvar_baseline = "BASE",
                  ggtheme = nestcolor::theme_nest(),
                  plotting_choices = c("all_in_one", "split_by_max_obs", "separate_by_obs"),
                  max_obs_per_plot = 4,
                  col = NULL) {
  checkmate::assert_count(max_obs_per_plot)
  checkmate::assert_subset(plotting_choices, c("all_in_one", "split_by_max_obs", "separate_by_obs"))
  checkmate::assert_character(col, null.ok = TRUE)

  plotting_choices <- match.arg(plotting_choices)

  if (plotting_choices == "all_in_one") {
    p <- h_g_ipp(
      df = df,
      xvar = xvar,
      yvar = yvar,
      xlab = xlab,
      ylab = ylab,
      id_var = id_var,
      title = title,
      subtitle = subtitle,
      caption = caption,
      add_baseline_hline = add_baseline_hline,
      yvar_baseline = yvar_baseline,
      ggtheme = ggtheme,
      col = col
    )

    return(p)
  } else if (plotting_choices == "split_by_max_obs") {
    id_vec <- unique(df[[id_var]])
    id_list <- split(
      id_vec,
      rep(1:ceiling(length(id_vec) / max_obs_per_plot),
        each = max_obs_per_plot,
        length.out = length(id_vec)
      )
    )

    df_list <- list()
    plot_list <- list()

    for (i in seq_along(id_list)) {
      df_list[[i]] <- df[df[[id_var]] %in% id_list[[i]], ]

      plots <- h_g_ipp(
        df = df_list[[i]],
        xvar = xvar,
        yvar = yvar,
        xlab = xlab,
        ylab = ylab,
        id_var = id_var,
        title = title,
        subtitle = subtitle,
        caption = caption,
        add_baseline_hline = add_baseline_hline,
        yvar_baseline = yvar_baseline,
        ggtheme = ggtheme,
        col = col
      )

      plot_list[[i]] <- plots
    }
    return(plot_list)
  } else {
    ind_df <- split(df, df[[id_var]])
    plot_list <- lapply(
      ind_df,
      function(x) {
        h_g_ipp(
          df = x,
          xvar = xvar,
          yvar = yvar,
          xlab = xlab,
          ylab = ylab,
          id_var = id_var,
          title = title,
          subtitle = subtitle,
          caption = caption,
          add_baseline_hline = add_baseline_hline,
          yvar_baseline = yvar_baseline,
          ggtheme = ggtheme,
          col = col
        )
      }
    )

    return(plot_list)
  }
}

#' Additional assertions to use with `checkmate`
#'
#' Additional assertion functions which can be used together with the `checkmate` package.
#'
#' @inheritParams checkmate::assert_factor
#' @param x (`any`)\cr object to test.
#' @param df (`data.frame`)\cr data set to test.
#' @param variables (named `list` of `character`)\cr list of variables to test.
#' @param include_boundaries (`flag`)\cr whether to include boundaries when testing
#'   for proportions.
#' @param na_level (`string`)\cr the string you have been using to represent NA or
#'   missing data. For `NA` values please consider using directly [is.na()] or
#'   similar approaches.
#'
#' @return Nothing if assertion passes, otherwise prints the error message.
#'
#' @name assertions
NULL

check_list_of_variables <- function(x) {
  # drop NULL elements in list
  x <- Filter(Negate(is.null), x)

  res <- checkmate::check_list(x,
    names = "named",
    min.len = 1,
    any.missing = FALSE,
    types = "character"
  )
  # no empty strings allowed
  if (isTRUE(res)) {
    res <- checkmate::check_character(unlist(x), min.chars = 1)
  }
  return(res)
}
#' @describeIn assertions Checks whether `x` is a valid list of variable names.
#'   `NULL` elements of the list `x` are dropped with `Filter(Negate(is.null), x)`.
#'
#' @keywords internal
assert_list_of_variables <- checkmate::makeAssertionFunction(check_list_of_variables)

check_df_with_variables <- function(df, variables, na_level = NULL) {
  checkmate::assert_data_frame(df)
  assert_list_of_variables(variables)

  # flag for equal variables and column names
  err_flag <- all(unlist(variables) %in% colnames(df))
  checkmate::assert_flag(err_flag)

  if (isFALSE(err_flag)) {
    vars <- setdiff(unlist(variables), colnames(df))
    return(paste(
      deparse(substitute(df)),
      "does not contain all specified variables as column names. Missing from data frame:",
      paste(vars, collapse = ", ")
    ))
  }
  # checking if na_level is present and in which column
  if (!is.null(na_level)) {
    checkmate::assert_string(na_level)
    res <- unlist(lapply(as.list(df)[unlist(variables)], function(x) any(x == na_level)))
    if (any(res)) {
      return(paste0(
        deparse(substitute(df)), " contains explicit na_level (", na_level,
        ") in the following columns: ", paste0(unlist(variables)[res],
          collapse = ", "
        )
      ))
    }
  }
  return(TRUE)
}
#' @describeIn assertions Check whether `df` is a data frame with the analysis `variables`.
#'   Please notice how this produces an error when not all variables are present in the
#'   data.frame while the opposite is not required.
#'
#' @keywords internal
assert_df_with_variables <- checkmate::makeAssertionFunction(check_df_with_variables)

check_valid_factor <- function(x,
                               min.levels = 1, # nolint
                               max.levels = NULL, # nolint
                               null.ok = TRUE, # nolint
                               any.missing = TRUE, # nolint
                               n.levels = NULL, # nolint
                               len = NULL) {
  # checks on levels insertion
  checkmate::assert_int(min.levels, lower = 1)

  # main factor check
  res <- checkmate::check_factor(x,
    min.levels = min.levels,
    null.ok = null.ok,
    max.levels = max.levels,
    any.missing = any.missing,
    n.levels = n.levels
  )

  # no empty strings allowed
  if (isTRUE(res)) {
    res <- checkmate::check_character(levels(x), min.chars = 1)
  }

  return(res)
}
#' @describeIn assertions Check whether `x` is a valid factor (i.e. has levels and no empty
#'   string levels). Note that `NULL` and `NA` elements are allowed.
#'
#' @keywords internal
assert_valid_factor <- checkmate::makeAssertionFunction(check_valid_factor)

check_df_with_factors <- function(df,
                                  variables,
                                  min.levels = 1, # nolint
                                  max.levels = NULL, # nolint
                                  any.missing = TRUE, # nolint
                                  na_level = NULL) {
  res <- check_df_with_variables(df, variables, na_level)
  # checking if all the columns specified by variables are valid factors
  if (isTRUE(res)) {
    # searching the data.frame with selected columns (variables) as a list
    res <- lapply(
      X = as.list(df)[unlist(variables)],
      FUN = check_valid_factor,
      min.levels = min.levels,
      max.levels = max.levels,
      any.missing = any.missing
    )
    res_lo <- unlist(vapply(res, Negate(isTRUE), logical(1)))
    if (any(res_lo)) {
      return(paste0(
        deparse(substitute(df)), " does not contain only factor variables among:",
        "\n* Column `", paste0(unlist(variables)[res_lo],
          "` of the data.frame -> ", res[res_lo],
          collapse = "\n* "
        )
      ))
    } else {
      res <- TRUE
    }
  }
  return(res)
}

#' @describeIn assertions Check whether `df` is a data frame where the analysis `variables`
#'   are all factors. Note that the creation of `NA` by direct call of `factor()` will
#'   trim `NA` levels out of the vector list itself.
#'
#' @keywords internal
assert_df_with_factors <- checkmate::makeAssertionFunction(check_df_with_factors)

#' @describeIn assertions Check whether `x` is a proportion: number between 0 and 1.
#'
#' @keywords internal
assert_proportion_value <- function(x, include_boundaries = FALSE) {
  checkmate::assert_number(x, lower = 0, upper = 1)
  checkmate::assert_flag(include_boundaries)
  if (isFALSE(include_boundaries)) {
    checkmate::assert_true(x > 0)
    checkmate::assert_true(x < 1)
  }
}

#' Univariate formula special term
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The special term `univariate` indicate that the model should be fitted individually for
#' every variable included in univariate.
#'
#' @param x (`character`)\cr a vector of variable names separated by commas.
#'
#' @return When used within a model formula, produces univariate models for each variable provided.
#'
#' @details
#' If provided alongside with pairwise specification, the model
#' `y ~ ARM + univariate(SEX, AGE, RACE)` lead to the study and comparison of the models
#' + `y ~ ARM`
#' + `y ~ ARM + SEX`
#' + `y ~ ARM + AGE`
#' + `y ~ ARM + RACE`
#'
#' @export
univariate <- function(x) {
  structure(x, varname = deparse(substitute(x)))
}

# Get the right-hand-term of a formula
rht <- function(x) {
  checkmate::assert_formula(x)
  y <- as.character(rev(x)[[1]])
  return(y)
}

#' Hazard ratio estimation in interactions
#'
#' This function estimates the hazard ratios between arms when an interaction variable is given with
#' specific values.
#'
#' @param variable,given (`character(2)`)\cr names of the two variables in the interaction. We seek the estimation of
#'   the levels of `variable` given the levels of `given`.
#' @param lvl_var,lvl_given (`character`)\cr corresponding levels given by [levels()].
#' @param mmat (named `numeric`) a vector filled with `0`s used as a template to obtain the design matrix.
#' @param coef (`numeric`)\cr vector of estimated coefficients.
#' @param vcov (`matrix`)\cr variance-covariance matrix of underlying model.
#' @param conf_level (`proportion`)\cr confidence level of estimate intervals.
#'
#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)
#'   and Sex (F, M; reference Female). The model is abbreviated: y ~ Arm + Sex + Arm x Sex.
#'   The cox regression estimates the coefficients along with a variance-covariance matrix for:
#'
#'   - b1 (arm b), b2 (arm c)
#'   - b3 (sex m)
#'   - b4 (arm b: sex m), b5 (arm c: sex m)
#'
#'   Given that I want an estimation of the Hazard Ratio for arm C/sex M, the estimation
#'   will be given in reference to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5),
#'   therefore the interaction coefficient is given by b2 + b5 while the standard error is obtained
#'   as $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ for a confidence level of 0.95.
#'
#' @return A list of matrices (one per level of variable) with rows corresponding to the combinations of
#'   `variable` and `given`, with columns:
#'   * `coef_hat`: Estimation of the coefficient.
#'   * `coef_se`: Standard error of the estimation.
#'   * `hr`: Hazard ratio.
#'   * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.
#'
#' @seealso [s_cox_multivariate()].
#'
#' @examples
#' library(dplyr)
#' library(survival)
#'
#' ADSL <- tern_ex_adsl %>%
#'   filter(SEX %in% c("F", "M"))
#'
#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "PFS")
#' adtte$ARMCD <- droplevels(adtte$ARMCD)
#' adtte$SEX <- droplevels(adtte$SEX)
#'
#' mod <- coxph(
#'   formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,
#'   data = adtte
#' )
#'
#' mmat <- stats::model.matrix(mod)[1, ]
#' mmat[!mmat == 0] <- 0
#'
#' @keywords internal
estimate_coef <- function(variable, given,
                          lvl_var, lvl_given,
                          coef,
                          mmat,
                          vcov,
                          conf_level = 0.95) {
  var_lvl <- paste0(variable, lvl_var[-1]) # [-1]: reference level
  giv_lvl <- paste0(given, lvl_given)

  design_mat <- expand.grid(variable = var_lvl, given = giv_lvl)
  design_mat <- design_mat[order(design_mat$variable, design_mat$given), ]
  design_mat <- within(
    data = design_mat,
    expr = {
      inter <- paste0(variable, ":", given)
      rev_inter <- paste0(given, ":", variable)
    }
  )

  split_by_variable <- design_mat$variable
  interaction_names <- paste(design_mat$variable, design_mat$given, sep = "/")

  design_mat <- apply(
    X = design_mat, MARGIN = 1, FUN = function(x) {
      mmat[names(mmat) %in% x[-which(names(x) == "given")]] <- 1
      return(mmat)
    }
  )
  colnames(design_mat) <- interaction_names

  betas <- as.matrix(coef)

  coef_hat <- t(design_mat) %*% betas
  dimnames(coef_hat)[2] <- "coef"

  coef_se <- apply(design_mat, 2, function(x) {
    vcov_el <- as.logical(x)
    y <- vcov[vcov_el, vcov_el]
    y <- sum(y)
    y <- sqrt(y)
    return(y)
  })

  q_norm <- stats::qnorm((1 + conf_level) / 2)
  y <- cbind(coef_hat, `se(coef)` = coef_se)

  y <- apply(y, 1, function(x) {
    x["hr"] <- exp(x["coef"])
    x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
    x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])

    return(x)
  })

  y <- t(y)
  y <- by(y, split_by_variable, identity)
  y <- lapply(y, as.matrix)

  attr(y, "details") <- paste0(
    "Estimations of ", variable,
    " hazard ratio given the level of ", given, " compared to ",
    variable, " level ", lvl_var[1], "."
  )
  return(y)
}

#' `tryCatch` around `car::Anova`
#'
#' Captures warnings when executing [car::Anova].
#'
#' @inheritParams car::Anova
#'
#' @return A list with item `aov` for the result of the model and `error_text` for the captured warnings.
#'
#' @examples
#' # `car::Anova` on cox regression model including strata and expected
#' # a likelihood ratio test triggers a warning as only Wald method is
#' # accepted.
#'
#' library(survival)
#'
#' mod <- coxph(
#'   formula = Surv(time = futime, event = fustat) ~ factor(rx) + strata(ecog.ps),
#'   data = ovarian
#' )
#'
#' @keywords internal
try_car_anova <- function(mod,
                          test.statistic) { # nolint
  y <- tryCatch(
    withCallingHandlers(
      expr = {
        warn_text <- c()
        list(
          aov = car::Anova(
            mod,
            test.statistic = test.statistic,
            type = "III"
          ),
          warn_text = warn_text
        )
      },
      warning = function(w) {
        # If a warning is detected it is handled as "w".
        warn_text <<- trimws(paste0("Warning in `try_car_anova`: ", w))

        # A warning is sometimes expected, then, we want to restart
        # the execution while ignoring the warning.
        invokeRestart("muffleWarning")
      }
    ),
    finally = {
    }
  )

  return(y)
}

#' Fit a Cox regression model and ANOVA
#'
#' The functions derives the effect p-values using [car::Anova()] from [survival::coxph()] results.
#'
#' @inheritParams t_coxreg
#'
#' @return A list with items `mod` (results of [survival::coxph()]), `msum` (result of `summary`) and
#'   `aov` (result of [car::Anova()]).
#'
#' @noRd
fit_n_aov <- function(formula,
                      data = data,
                      conf_level = conf_level,
                      pval_method = c("wald", "likelihood"),
                      ...) {
  pval_method <- match.arg(pval_method)

  environment(formula) <- environment()
  suppressWarnings({
    # We expect some warnings due to coxph which fails strict programming.
    mod <- survival::coxph(formula, data = data, ...)
    msum <- summary(mod, conf.int = conf_level)
  })

  aov <- try_car_anova(
    mod,
    test.statistic = switch(pval_method,
      "wald" = "Wald",
      "likelihood" = "LR"
    )
  )

  warn_attr <- aov$warn_text
  if (!is.null(aov$warn_text)) message(warn_attr)

  aov <- aov$aov
  y <- list(mod = mod, msum = msum, aov = aov)
  attr(y, "message") <- warn_attr

  return(y)
}

# argument_checks
check_formula <- function(formula) {
  if (!(inherits(formula, "formula"))) {
    stop("Check `formula`. A formula should resemble `Surv(time = AVAL, event = 1 - CNSR) ~ study_arm(ARMCD)`.")
  }

  invisible()
}

check_covariate_formulas <- function(covariates) {
  if (!all(vapply(X = covariates, FUN = inherits, what = "formula", FUN.VALUE = TRUE)) || is.null(covariates)) {
    stop("Check `covariates`, it should be a list of right-hand-term formulas, e.g. list(Age = ~AGE).")
  }

  invisible()
}

name_covariate_names <- function(covariates) {
  miss_names <- names(covariates) == ""
  no_names <- is.null(names(covariates))
  if (any(miss_names)) names(covariates)[miss_names] <- vapply(covariates[miss_names], FUN = rht, FUN.VALUE = "name")
  if (no_names) names(covariates) <- vapply(covariates, FUN = rht, FUN.VALUE = "name")
  return(covariates)
}

check_increments <- function(increments, covariates) {
  if (!is.null(increments)) {
    covariates <- vapply(covariates, FUN = rht, FUN.VALUE = "name")
    lapply(
      X = names(increments), FUN = function(x) {
        if (!x %in% covariates) {
          warning(
            paste(
              "Check `increments`, the `increment` for ", x,
              "doesn't match any names in investigated covariate(s)."
            )
          )
        }
      }
    )
  }

  invisible()
}

#' Multivariate Cox model - summarized results
#'
#' Analyses based on multivariate Cox model are usually not performed for the Controlled Substance Reporting or
#' regulatory documents but serve exploratory purposes only (e.g., for publication). In practice, the model usually
#' includes only the main effects (without interaction terms). It produces the hazard ratio estimates for each of the
#' covariates included in the model.
#' The analysis follows the same principles (e.g., stratified vs. unstratified analysis and tie handling) as the
#' usual Cox model analysis. Since there is usually no pre-specified hypothesis testing for such analysis,
#' the p.values need to be interpreted with caution. (**Statistical Analysis of Clinical Trials Data with R**,
#' `NEST's bookdown`)
#'
#' @param formula (`formula`)\cr a formula corresponding to the investigated [survival::Surv()] survival model
#'   including covariates.
#' @param data (`data.frame`)\cr a data frame which includes the variable in formula and covariates.
#' @param conf_level (`proportion`)\cr the confidence level for the hazard ratio interval estimations. Default is 0.95.
#' @param pval_method (`string`)\cr the method used for the estimation of p-values, should be one of
#'   `"wald"` (default) or `"likelihood"`.
#' @param ... optional parameters passed to [survival::coxph()]. Can include `ties`, a character string specifying the
#'   method for tie handling, one of `exact` (default), `efron`, `breslow`.
#'
#' @return A `list` with elements `mod`, `msum`, `aov`, and `coef_inter`.
#'
#' @details The output is limited to single effect terms. Work in ongoing for estimation of interaction terms
#'   but is out of scope as defined by the  Global Data Standards Repository
#'   (**`GDS_Standard_TLG_Specs_Tables_2.doc`**).
#'
#' @seealso [estimate_coef()].
#'
#' @examples
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte
#' adtte_f <- subset(adtte, PARAMCD == "OS") # _f: filtered
#' adtte_f <- filter(
#'   adtte_f,
#'   PARAMCD == "OS" &
#'     SEX %in% c("F", "M") &
#'     RACE %in% c("ASIAN", "BLACK OR AFRICAN AMERICAN", "WHITE")
#' )
#' adtte_f$SEX <- droplevels(adtte_f$SEX)
#' adtte_f$RACE <- droplevels(adtte_f$RACE)
#'
#' @keywords internal
s_cox_multivariate <- function(formula, data,
                               conf_level = 0.95,
                               pval_method = c("wald", "likelihood"),
                               ...) {
  tf <- stats::terms(formula, specials = c("strata"))
  covariates <- rownames(attr(tf, "factors"))[-c(1, unlist(attr(tf, "specials")))]
  lapply(
    X = covariates,
    FUN = function(x) {
      if (is.character(data[[x]])) {
        data[[x]] <<- as.factor(data[[x]])
      }
      invisible()
    }
  )
  pval_method <- match.arg(pval_method)

  # Results directly exported from environment(fit_n_aov) to environment(s_function_draft)
  y <- fit_n_aov(
    formula = formula,
    data = data,
    conf_level = conf_level,
    pval_method = pval_method,
    ...
  )
  mod <- y$mod
  aov <- y$aov
  msum <- y$msum
  list2env(as.list(y), environment())

  all_term_labs <- attr(mod$terms, "term.labels")
  term_labs <- all_term_labs[which(attr(mod$terms, "order") == 1)]
  names(term_labs) <- term_labs

  coef_inter <- NULL
  if (any(attr(mod$terms, "order") > 1)) {
    for_inter <- all_term_labs[attr(mod$terms, "order") > 1]
    names(for_inter) <- for_inter
    mmat <- stats::model.matrix(mod)[1, ]
    mmat[!mmat == 0] <- 0
    mcoef <- stats::coef(mod)
    mvcov <- stats::vcov(mod)

    estimate_coef_local <- function(variable, given) {
      estimate_coef(
        variable, given,
        coef = mcoef, mmat = mmat, vcov = mvcov, conf_level = conf_level,
        lvl_var = levels(data[[variable]]), lvl_given = levels(data[[given]])
      )
    }

    coef_inter <- lapply(
      for_inter, function(x) {
        y <- attr(mod$terms, "factors")[, x]
        y <- names(y[y > 0])
        Map(estimate_coef_local, variable = y, given = rev(y))
      }
    )
  }

  list(mod = mod, msum = msum, aov = aov, coef_inter = coef_inter)
}

#' Count number of patients
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [analyze_num_patients()] creates a layout element to count total numbers of unique or
#' non-unique patients. The primary analysis variable `vars` is used to uniquely identify patients.
#'
#' The `count_by` variable can be used to identify non-unique patients such that the number of patients with a unique
#' combination of values in `vars` and `count_by` will be returned instead as the `nonunique` statistic. The `required`
#' variable can be used to specify a variable required to be non-missing for the record to be included in the counts.
#'
#' The summarize function [summarize_num_patients()] performs the same function as [analyze_num_patients()] except it
#' creates content rows, not data rows, to summarize the current table row/column context and operates on the level of
#' the latest row split or the root of the table if no row splits have occurred.
#'
#' @inheritParams argument_convention
#' @param required (`character` or `NULL`)\cr name of a variable that is required to be non-missing.
#' @param count_by (`character` or `NULL`)\cr name of a variable to be combined with `vars` when counting
#'   `nonunique` records.
#' @param unique_count_suffix (`flag`)\cr whether the `"(n)"` suffix should be added to `unique_count` labels.
#'   Defaults to `TRUE`.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("summarize_num_patients"), type = "sh")``
#'
#' @name summarize_num_patients
#' @order 1
NULL

#' @describeIn summarize_num_patients Statistics function which counts the number of
#'   unique patients, the corresponding percentage taken with respect to the
#'   total number of patients, and the number of non-unique patients.
#'
#' @param x (`character` or `factor`)\cr vector of patient IDs.
#'
#' @return
#' * `s_num_patients()` returns a named `list` of 3 statistics:
#'   * `unique`: Vector of counts and percentages.
#'   * `nonunique`: Vector of counts.
#'   * `unique_count`: Counts.
#'
#' @examples
#' # Use the statistics function to count number of unique and nonunique patients.
#' s_num_patients(x = as.character(c(1, 1, 1, 2, 4, NA)), labelstr = "", .N_col = 6L)
#' s_num_patients(
#'   x = as.character(c(1, 1, 1, 2, 4, NA)),
#'   labelstr = "",
#'   .N_col = 6L,
#'   count_by = c(1, 1, 2, 1, 1, 1)
#' )
#'
#' @export
s_num_patients <- function(x, labelstr, .N_col, count_by = NULL, unique_count_suffix = TRUE) { # nolint

  checkmate::assert_string(labelstr)
  checkmate::assert_count(.N_col)
  checkmate::assert_multi_class(x, classes = c("factor", "character"))
  checkmate::assert_flag(unique_count_suffix)

  count1 <- n_available(unique(x))
  count2 <- n_available(x)

  if (!is.null(count_by)) {
    checkmate::assert_vector(count_by, len = length(x))
    count2 <- n_available(unique(interaction(x, count_by)))
  }

  out <- list(
    unique = formatters::with_label(c(count1, ifelse(count1 == 0 && .N_col == 0, 0, count1 / .N_col)), labelstr),
    nonunique = formatters::with_label(count2, labelstr),
    unique_count = formatters::with_label(
      count1, ifelse(unique_count_suffix, paste0(labelstr, if (nzchar(labelstr)) " ", "(n)"), labelstr)
    )
  )

  out
}

#' @describeIn summarize_num_patients Statistics function which counts the number of unique patients
#'   in a column (variable), the corresponding percentage taken with respect to the total number of
#'   patients, and the number of non-unique patients in the column.
#'
#' @return
#' * `s_num_patients_content()` returns the same values as `s_num_patients()`.
#'
#' @examples
#' # Count number of unique and non-unique patients.
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(1, 2, 1, 4, NA)),
#'   EVENT = as.character(c(10, 15, 10, 17, 8))
#' )
#' s_num_patients_content(df, .N_col = 5, .var = "USUBJID")
#'
#' df_by_event <- data.frame(
#'   USUBJID = as.character(c(1, 2, 1, 4, NA)),
#'   EVENT = c(10, 15, 10, 17, 8)
#' )
#' s_num_patients_content(df_by_event, .N_col = 5, .var = "USUBJID", count_by = "EVENT")
#'
#' @export
s_num_patients_content <- function(df,
                                   labelstr = "",
                                   .N_col, # nolint
                                   .var,
                                   required = NULL,
                                   count_by = NULL,
                                   unique_count_suffix = TRUE) {
  checkmate::assert_string(.var)
  checkmate::assert_data_frame(df)
  if (is.null(count_by)) {
    assert_df_with_variables(df, list(id = .var))
  } else {
    assert_df_with_variables(df, list(id = .var, count_by = count_by))
  }
  if (!is.null(required)) {
    checkmate::assert_string(required)
    assert_df_with_variables(df, list(required = required))
    df <- df[!is.na(df[[required]]), , drop = FALSE]
  }

  x <- df[[.var]]
  y <- if (is.null(count_by)) NULL else df[[count_by]]

  s_num_patients(
    x = x,
    labelstr = labelstr,
    .N_col = .N_col,
    count_by = y,
    unique_count_suffix = unique_count_suffix
  )
}

c_num_patients <- make_afun(
  s_num_patients_content,
  .stats = c("unique", "nonunique", "unique_count"),
  .formats = c(unique = format_count_fraction_fixed_dp, nonunique = "xx", unique_count = "xx")
)

#' @describeIn summarize_num_patients Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_num_patients()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_num_patients_content()` to the table layout.
#'
#' @examples
#' # summarize_num_patients
#' tbl <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("SEX") %>%
#'   summarize_num_patients("USUBJID", .stats = "unique_count") %>%
#'   build_table(df)
#'
#' tbl
#'
#' @export
#' @order 3
summarize_num_patients <- function(lyt,
                                   var,
                                   required = NULL,
                                   count_by = NULL,
                                   unique_count_suffix = TRUE,
                                   na_str = default_na_str(),
                                   .stats = NULL,
                                   .formats = NULL,
                                   .labels = c(
                                     unique = "Number of patients with at least one event",
                                     nonunique = "Number of events"
                                   ),
                                   .indent_mods = 0L,
                                   riskdiff = FALSE,
                                   ...) {
  checkmate::assert_flag(riskdiff)

  if (is.null(.stats)) .stats <- c("unique", "nonunique", "unique_count")
  if (length(.labels) > length(.stats)) .labels <- .labels[names(.labels) %in% .stats]

  s_args <- list(required = required, count_by = count_by, unique_count_suffix = unique_count_suffix, ...)

  cfun <- make_afun(
    c_num_patients,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_num_patients_content" = cfun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = ifelse(isFALSE(riskdiff), cfun, afun_riskdiff),
    na_str = na_str,
    extra_args = extra_args,
    indent_mod = .indent_mods
  )
}

#' @describeIn summarize_num_patients Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `analyze_num_patients()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_num_patients_content()` to the table layout.
#'
#' @details In general, functions that starts with `analyze*` are expected to
#'   work like [rtables::analyze()], while functions that starts with `summarize*`
#'   are based upon [rtables::summarize_row_groups()]. The latter provides a
#'   value for each dividing split in the row and column space, but, being it
#'   bound to the fundamental splits, it is repeated by design in every page
#'   when pagination is involved.
#'
#' @note As opposed to [summarize_num_patients()], this function does not repeat the produced rows.
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = as.character(c(1, 2, 1, 4, NA, 6, 6, 8, 9)),
#'   ARM = c("A", "A", "A", "A", "A", "B", "B", "B", "B"),
#'   AGE = c(10, 15, 10, 17, 8, 11, 11, 19, 17),
#'   SEX = c("M", "M", "M", "F", "F", "F", "M", "F", "M")
#' )
#'
#' # analyze_num_patients
#' tbl <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   analyze_num_patients("USUBJID", .stats = c("unique")) %>%
#'   build_table(df)
#'
#' tbl
#'
#' @export
#' @order 2
analyze_num_patients <- function(lyt,
                                 vars,
                                 required = NULL,
                                 count_by = NULL,
                                 unique_count_suffix = TRUE,
                                 na_str = default_na_str(),
                                 nested = TRUE,
                                 .stats = NULL,
                                 .formats = NULL,
                                 .labels = c(
                                   unique = "Number of patients with at least one event",
                                   nonunique = "Number of events"
                                 ),
                                 show_labels = c("default", "visible", "hidden"),
                                 .indent_mods = 0L,
                                 riskdiff = FALSE,
                                 ...) {
  checkmate::assert_flag(riskdiff)

  if (is.null(.stats)) .stats <- c("unique", "nonunique", "unique_count")
  if (length(.labels) > length(.stats)) .labels <- .labels[names(.labels) %in% .stats]

  s_args <- list(required = required, count_by = count_by, unique_count_suffix = unique_count_suffix, ...)

  afun <- make_afun(
    c_num_patients,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels
  )

  extra_args <- if (isFALSE(riskdiff)) {
    s_args
  } else {
    list(
      afun = list("s_num_patients_content" = afun),
      .stats = .stats,
      .indent_mods = .indent_mods,
      s_args = s_args
    )
  }

  analyze(
    afun = ifelse(isFALSE(riskdiff), afun, afun_riskdiff),
    lyt = lyt,
    vars = vars,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    indent_mod = .indent_mods
  )
}

#' Proportion difference estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analysis function [estimate_proportion_diff()] creates a layout element to estimate the difference in proportion
#' of responders within a studied population. The primary analysis variable, `vars`, is a logical variable indicating
#' whether a response has occurred for each record. See the `method` parameter for options of methods to use when
#' constructing the confidence interval of the proportion difference. A stratification variable can be supplied via the
#' `strata` element of the `variables` argument.
#'
#'
#' @inheritParams prop_diff_strat_nc
#' @inheritParams argument_convention
#' @param method (`string`)\cr the method used for the confidence interval estimation.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("estimate_proportion_diff"), type = "sh")``
#'
#' @seealso [d_proportion_diff()]
#'
#' @name prop_diff
#' @order 1
NULL

#' @describeIn prop_diff Statistics function estimating the difference
#'   in terms of responder proportion.
#'
#' @return
#' * `s_proportion_diff()` returns a named list of elements `diff` and `diff_ci`.
#'
#' @note When performing an unstratified analysis, methods `"cmh"`, `"strat_newcombe"`, and `"strat_newcombecc"` are
#'   not permitted.
#'
#' @examples
#' s_proportion_diff(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   conf_level = 0.90,
#'   method = "ha"
#' )
#'
#' # CMH example with strata
#' s_proportion_diff(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   variables = list(strata = c("f1", "f2")),
#'   conf_level = 0.90,
#'   method = "cmh"
#' )
#'
#' @export
s_proportion_diff <- function(df,
                              .var,
                              .ref_group,
                              .in_ref_col,
                              variables = list(strata = NULL),
                              conf_level = 0.95,
                              method = c(
                                "waldcc", "wald", "cmh",
                                "ha", "newcombe", "newcombecc",
                                "strat_newcombe", "strat_newcombecc"
                              ),
                              weights_method = "cmh") {
  method <- match.arg(method)
  if (is.null(variables$strata) && checkmate::test_subset(method, c("cmh", "strat_newcombe", "strat_newcombecc"))) {
    stop(paste(
      "When performing an unstratified analysis, methods 'cmh', 'strat_newcombe', and 'strat_newcombecc' are not",
      "permitted. Please choose a different method."
    ))
  }
  y <- list(diff = "", diff_ci = "")

  if (!.in_ref_col) {
    rsp <- c(.ref_group[[.var]], df[[.var]])
    grp <- factor(
      rep(
        c("ref", "Not-ref"),
        c(nrow(.ref_group), nrow(df))
      ),
      levels = c("ref", "Not-ref")
    )

    if (!is.null(variables$strata)) {
      strata_colnames <- variables$strata
      checkmate::assert_character(strata_colnames, null.ok = FALSE)
      strata_vars <- stats::setNames(as.list(strata_colnames), strata_colnames)

      assert_df_with_variables(df, strata_vars)
      assert_df_with_variables(.ref_group, strata_vars)

      # Merging interaction strata for reference group rows data and remaining
      strata <- c(
        interaction(.ref_group[strata_colnames]),
        interaction(df[strata_colnames])
      )
      strata <- as.factor(strata)
    }

    # Defining the std way to calculate weights for strat_newcombe
    if (!is.null(variables$weights_method)) {
      weights_method <- variables$weights_method
    } else {
      weights_method <- "cmh"
    }

    y <- switch(method,
      "wald" = prop_diff_wald(rsp, grp, conf_level, correct = FALSE),
      "waldcc" = prop_diff_wald(rsp, grp, conf_level, correct = TRUE),
      "ha" = prop_diff_ha(rsp, grp, conf_level),
      "newcombe" = prop_diff_nc(rsp, grp, conf_level, correct = FALSE),
      "newcombecc" = prop_diff_nc(rsp, grp, conf_level, correct = TRUE),
      "strat_newcombe" = prop_diff_strat_nc(rsp,
        grp,
        strata,
        weights_method,
        conf_level,
        correct = FALSE
      ),
      "strat_newcombecc" = prop_diff_strat_nc(rsp,
        grp,
        strata,
        weights_method,
        conf_level,
        correct = TRUE
      ),
      "cmh" = prop_diff_cmh(rsp, grp, strata, conf_level)[c("diff", "diff_ci")]
    )

    y$diff <- y$diff * 100
    y$diff_ci <- y$diff_ci * 100
  }

  attr(y$diff, "label") <- "Difference in Response rate (%)"
  attr(y$diff_ci, "label") <- d_proportion_diff(
    conf_level, method,
    long = FALSE
  )

  y
}

#' @describeIn prop_diff Formatted analysis function which is used as `afun` in `estimate_proportion_diff()`.
#'
#' @return
#' * `a_proportion_diff()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_proportion_diff(
#'   df = subset(dta, grp == "A"),
#'   .var = "rsp",
#'   .ref_group = subset(dta, grp == "B"),
#'   .in_ref_col = FALSE,
#'   conf_level = 0.90,
#'   method = "ha"
#' )
#'
#' @export
a_proportion_diff <- make_afun(
  s_proportion_diff,
  .formats = c(diff = "xx.x", diff_ci = "(xx.x, xx.x)"),
  .indent_mods = c(diff = 0L, diff_ci = 1L)
)

#' @describeIn prop_diff Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_proportion_diff()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_proportion_diff()` to the table layout.
#'
#' @examples
#' ## "Mid" case: 4/4 respond in group A, 1/2 respond in group B.
#' nex <- 100 # Number of example rows
#' dta <- data.frame(
#'   "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
#'   "grp" = sample(c("A", "B"), nex, TRUE),
#'   "f1" = sample(c("a1", "a2"), nex, TRUE),
#'   "f2" = sample(c("x", "y", "z"), nex, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' l <- basic_table() %>%
#'   split_cols_by(var = "grp", ref_group = "B") %>%
#'   estimate_proportion_diff(
#'     vars = "rsp",
#'     conf_level = 0.90,
#'     method = "ha"
#'   )
#'
#' build_table(l, df = dta)
#'
#' @export
#' @order 2
estimate_proportion_diff <- function(lyt,
                                     vars,
                                     variables = list(strata = NULL),
                                     conf_level = 0.95,
                                     method = c(
                                       "waldcc", "wald", "cmh",
                                       "ha", "newcombe", "newcombecc",
                                       "strat_newcombe", "strat_newcombecc"
                                     ),
                                     weights_method = "cmh",
                                     na_str = default_na_str(),
                                     nested = TRUE,
                                     ...,
                                     var_labels = vars,
                                     show_labels = "hidden",
                                     table_names = vars,
                                     .stats = NULL,
                                     .formats = NULL,
                                     .labels = NULL,
                                     .indent_mods = NULL) {
  extra_args <- list(
    variables = variables, conf_level = conf_level, method = method, weights_method = weights_method, ...
  )

  afun <- make_afun(
    a_proportion_diff,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    afun = afun,
    var_labels = var_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Check proportion difference arguments
#'
#' Verifies that and/or convert arguments into valid values to be used in the
#' estimation of difference in responder proportions.
#'
#' @inheritParams prop_diff
#' @inheritParams prop_diff_wald
#'
#' @keywords internal
check_diff_prop_ci <- function(rsp,
                               grp,
                               strata = NULL,
                               conf_level,
                               correct = NULL) {
  checkmate::assert_logical(rsp, any.missing = FALSE)
  checkmate::assert_factor(grp, len = length(rsp), any.missing = FALSE, n.levels = 2)
  checkmate::assert_number(conf_level, lower = 0, upper = 1)
  checkmate::assert_flag(correct, null.ok = TRUE)

  if (!is.null(strata)) {
    checkmate::assert_factor(strata, len = length(rsp))
  }

  invisible()
}

#' Description of method used for proportion comparison
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function that describes the analysis in
#' [s_proportion_diff()].
#'
#' @inheritParams s_proportion_diff
#' @param long (`flag`)\cr whether a long (`TRUE`) or a short (`FALSE`, default) description is required.
#'
#' @return A `string` describing the analysis.
#'
#' @seealso [prop_diff]
#'
#' @export
d_proportion_diff <- function(conf_level,
                              method,
                              long = FALSE) {
  label <- paste0(conf_level * 100, "% CI")
  if (long) {
    label <- paste(
      label,
      ifelse(
        method == "cmh",
        "for adjusted difference",
        "for difference"
      )
    )
  }

  method_part <- switch(method,
    "cmh" = "CMH, without correction",
    "waldcc" = "Wald, with correction",
    "wald" = "Wald, without correction",
    "ha" = "Anderson-Hauck",
    "newcombe" = "Newcombe, without correction",
    "newcombecc" = "Newcombe, with correction",
    "strat_newcombe" = "Stratified Newcombe, without correction",
    "strat_newcombecc" = "Stratified Newcombe, with correction",
    stop(paste(method, "does not have a description"))
  )
  paste0(label, " (", method_part, ")")
}

#' Helper functions to calculate proportion difference
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @inheritParams prop_diff
#' @param grp (`factor`)\cr vector assigning observations to one out of two groups
#'   (e.g. reference and treatment group).
#'
#' @return A named `list` of elements `diff` (proportion difference) and `diff_ci`
#'   (proportion difference confidence interval).
#'
#' @seealso [prop_diff()] for implementation of these helper functions.
#'
#' @name h_prop_diff
NULL

#' @describeIn h_prop_diff The Wald interval follows the usual textbook
#'   definition for a single proportion confidence interval using the normal
#'   approximation. It is possible to include a continuity correction for Wald's
#'   interval.
#'
#' @param correct (`flag`)\cr whether to include the continuity correction. For further
#'   information, see [stats::prop.test()].
#'
#' @examples
#' # Wald confidence interval
#' set.seed(2)
#' rsp <- sample(c(TRUE, FALSE), replace = TRUE, size = 20)
#' grp <- factor(c(rep("A", 10), rep("B", 10)))
#'
#' prop_diff_wald(rsp = rsp, grp = grp, conf_level = 0.95, correct = FALSE)
#'
#' @export
prop_diff_wald <- function(rsp,
                           grp,
                           conf_level = 0.95,
                           correct = FALSE) {
  if (isTRUE(correct)) {
    mthd <- "waldcc"
  } else {
    mthd <- "wald"
  }
  grp <- as_factor_keep_attributes(grp)
  check_diff_prop_ci(
    rsp = rsp, grp = grp, conf_level = conf_level, correct = correct
  )

  # check if binary response is coded as logical
  checkmate::assert_logical(rsp, any.missing = FALSE)
  checkmate::assert_factor(grp, len = length(rsp), any.missing = FALSE, n.levels = 2)

  tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
  # x1 and n1 are non-reference groups.
  diff_ci <- desctools_binom(
    x1 = tbl[2], n1 = sum(tbl[2], tbl[4]),
    x2 = tbl[1], n2 = sum(tbl[1], tbl[3]),
    conf.level = conf_level,
    method = mthd
  )

  list(
    "diff" = unname(diff_ci[, "est"]),
    "diff_ci" = unname(diff_ci[, c("lwr.ci", "upr.ci")])
  )
}

#' @describeIn h_prop_diff Anderson-Hauck confidence interval.
#'
#' @examples
#' # Anderson-Hauck confidence interval
#' ## "Mid" case: 3/4 respond in group A, 1/2 respond in group B.
#' rsp <- c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE)
#' grp <- factor(c("A", "B", "A", "B", "A", "A"), levels = c("B", "A"))
#'
#' prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.90)
#'
#' ## Edge case: Same proportion of response in A and B.
#' rsp <- c(TRUE, FALSE, TRUE, FALSE)
#' grp <- factor(c("A", "A", "B", "B"), levels = c("A", "B"))
#'
#' prop_diff_ha(rsp = rsp, grp = grp, conf_level = 0.6)
#'
#' @export
prop_diff_ha <- function(rsp,
                         grp,
                         conf_level) {
  grp <- as_factor_keep_attributes(grp)
  check_diff_prop_ci(rsp = rsp, grp = grp, conf_level = conf_level)

  tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
  # x1 and n1 are non-reference groups.
  ci <- desctools_binom(
    x1 = tbl[2], n1 = sum(tbl[2], tbl[4]),
    x2 = tbl[1], n2 = sum(tbl[1], tbl[3]),
    conf.level = conf_level,
    method = "ha"
  )
  list(
    "diff" = unname(ci[, "est"]),
    "diff_ci" = unname(ci[, c("lwr.ci", "upr.ci")])
  )
}

#' @describeIn h_prop_diff Newcombe confidence interval. It is based on
#'   the Wilson score confidence interval for a single binomial proportion.
#'
#' @examples
#' # Newcombe confidence interval
#'
#' set.seed(1)
#' rsp <- c(
#'   sample(c(TRUE, FALSE), size = 40, prob = c(3 / 4, 1 / 4), replace = TRUE),
#'   sample(c(TRUE, FALSE), size = 40, prob = c(1 / 2, 1 / 2), replace = TRUE)
#' )
#' grp <- factor(rep(c("A", "B"), each = 40), levels = c("B", "A"))
#' table(rsp, grp)
#'
#' prop_diff_nc(rsp = rsp, grp = grp, conf_level = 0.9)
#'
#' @export
prop_diff_nc <- function(rsp,
                         grp,
                         conf_level,
                         correct = FALSE) {
  if (isTRUE(correct)) {
    mthd <- "scorecc"
  } else {
    mthd <- "score"
  }
  grp <- as_factor_keep_attributes(grp)
  check_diff_prop_ci(rsp = rsp, grp = grp, conf_level = conf_level)

  p_grp <- tapply(rsp, grp, mean)
  diff_p <- unname(diff(p_grp))
  tbl <- table(grp, factor(rsp, levels = c(TRUE, FALSE)))
  ci <- desctools_binom(
    # x1 and n1 are non-reference groups.
    x1 = tbl[2], n1 = sum(tbl[2], tbl[4]),
    x2 = tbl[1], n2 = sum(tbl[1], tbl[3]),
    conf.level = conf_level,
    method = mthd
  )
  list(
    "diff" = unname(ci[, "est"]),
    "diff_ci" = unname(ci[, c("lwr.ci", "upr.ci")])
  )
}

#' @describeIn h_prop_diff Calculates the weighted difference. This is defined as the difference in
#'   response rates between the experimental treatment group and the control treatment group, adjusted
#'   for stratification factors by applying Cochran-Mantel-Haenszel (CMH) weights. For the CMH chi-squared
#'   test, use [stats::mantelhaen.test()].
#'
#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
#'
#' @examples
#' # Cochran-Mantel-Haenszel confidence interval
#'
#' set.seed(2)
#' rsp <- sample(c(TRUE, FALSE), 100, TRUE)
#' grp <- sample(c("Placebo", "Treatment"), 100, TRUE)
#' grp <- factor(grp, levels = c("Placebo", "Treatment"))
#' strata_data <- data.frame(
#'   "f1" = sample(c("a", "b"), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' prop_diff_cmh(
#'   rsp = rsp, grp = grp, strata = interaction(strata_data),
#'   conf_level = 0.90
#' )
#'
#' @export
prop_diff_cmh <- function(rsp,
                          grp,
                          strata,
                          conf_level = 0.95) {
  grp <- as_factor_keep_attributes(grp)
  strata <- as_factor_keep_attributes(strata)
  check_diff_prop_ci(
    rsp = rsp, grp = grp, conf_level = conf_level, strata = strata
  )

  if (any(tapply(rsp, strata, length) < 5)) {
    warning("Less than 5 observations in some strata.")
  }

  # first dimension: FALSE, TRUE
  # 2nd dimension: CONTROL, TX
  # 3rd dimension: levels of strata
  # rsp as factor rsp to handle edge case of no FALSE (or TRUE) rsp records
  t_tbl <- table(
    factor(rsp, levels = c("FALSE", "TRUE")),
    grp,
    strata
  )
  n1 <- colSums(t_tbl[1:2, 1, ])
  n2 <- colSums(t_tbl[1:2, 2, ])
  p1 <- t_tbl[2, 1, ] / n1
  p2 <- t_tbl[2, 2, ] / n2
  # CMH weights
  use_stratum <- (n1 > 0) & (n2 > 0)
  n1 <- n1[use_stratum]
  n2 <- n2[use_stratum]
  p1 <- p1[use_stratum]
  p2 <- p2[use_stratum]
  wt <- (n1 * n2 / (n1 + n2))
  wt_normalized <- wt / sum(wt)
  est1 <- sum(wt_normalized * p1)
  est2 <- sum(wt_normalized * p2)
  estimate <- c(est1, est2)
  names(estimate) <- levels(grp)
  se1 <- sqrt(sum(wt_normalized^2 * p1 * (1 - p1) / n1))
  se2 <- sqrt(sum(wt_normalized^2 * p2 * (1 - p2) / n2))
  z <- stats::qnorm((1 + conf_level) / 2)
  err1 <- z * se1
  err2 <- z * se2
  ci1 <- c((est1 - err1), (est1 + err1))
  ci2 <- c((est2 - err2), (est2 + err2))
  estimate_ci <- list(ci1, ci2)
  names(estimate_ci) <- levels(grp)
  diff_est <- est2 - est1
  se_diff <- sqrt(sum(((p1 * (1 - p1) / n1) + (p2 * (1 - p2) / n2)) * wt_normalized^2))
  diff_ci <- c(diff_est - z * se_diff, diff_est + z * se_diff)

  list(
    prop = estimate,
    prop_ci = estimate_ci,
    diff = diff_est,
    diff_ci = diff_ci,
    weights = wt_normalized,
    n1 = n1,
    n2 = n2
  )
}

#' @describeIn h_prop_diff Calculates the stratified Newcombe confidence interval and difference in response
#'   rates between the experimental treatment group and the control treatment group, adjusted for stratification
#'   factors. This implementation follows closely the one proposed by \insertCite{Yan2010-jt;textual}{tern}.
#'   Weights can be estimated from the heuristic proposed in [prop_strat_wilson()] or from CMH-derived weights
#'   (see [prop_diff_cmh()]).
#'
#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
#' @param weights_method (`string`)\cr weights method. Can be either `"cmh"` or `"heuristic"`
#'   and directs the way weights are estimated.
#'
#' @references
#' \insertRef{Yan2010-jt}{tern}
#'
#' @examples
#' # Stratified Newcombe confidence interval
#'
#' set.seed(2)
#' data_set <- data.frame(
#'   "rsp" = sample(c(TRUE, FALSE), 100, TRUE),
#'   "f1" = sample(c("a", "b"), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   "grp" = sample(c("Placebo", "Treatment"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' prop_diff_strat_nc(
#'   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),
#'   weights_method = "cmh",
#'   conf_level = 0.90
#' )
#'
#' prop_diff_strat_nc(
#'   rsp = data_set$rsp, grp = data_set$grp, strata = interaction(data_set[2:3]),
#'   weights_method = "wilson_h",
#'   conf_level = 0.90
#' )
#'
#' @export
prop_diff_strat_nc <- function(rsp,
                               grp,
                               strata,
                               weights_method = c("cmh", "wilson_h"),
                               conf_level = 0.95,
                               correct = FALSE) {
  weights_method <- match.arg(weights_method)
  grp <- as_factor_keep_attributes(grp)
  strata <- as_factor_keep_attributes(strata)
  check_diff_prop_ci(
    rsp = rsp, grp = grp, conf_level = conf_level, strata = strata
  )
  checkmate::assert_number(conf_level, lower = 0, upper = 1)
  checkmate::assert_flag(correct)
  if (any(tapply(rsp, strata, length) < 5)) {
    warning("Less than 5 observations in some strata.")
  }

  rsp_by_grp <- split(rsp, f = grp)
  strata_by_grp <- split(strata, f = grp)

  # Finding the weights
  weights <- if (identical(weights_method, "cmh")) {
    prop_diff_cmh(rsp = rsp, grp = grp, strata = strata)$weights
  } else if (identical(weights_method, "wilson_h")) {
    prop_strat_wilson(rsp, strata, conf_level = conf_level, correct = correct)$weights
  }
  weights[levels(strata)[!levels(strata) %in% names(weights)]] <- 0

  # Calculating lower (`l`) and upper (`u`) confidence bounds per group.
  strat_wilson_by_grp <- Map(
    prop_strat_wilson,
    rsp = rsp_by_grp,
    strata = strata_by_grp,
    weights = list(weights, weights),
    conf_level = conf_level,
    correct = correct
  )

  ci_ref <- strat_wilson_by_grp[[1]]
  ci_trt <- strat_wilson_by_grp[[2]]
  l_ref <- as.numeric(ci_ref$conf_int[1])
  u_ref <- as.numeric(ci_ref$conf_int[2])
  l_trt <- as.numeric(ci_trt$conf_int[1])
  u_trt <- as.numeric(ci_trt$conf_int[2])

  # Estimating the diff and n_ref, n_trt (it allows different weights to be used)
  t_tbl <- table(
    factor(rsp, levels = c("FALSE", "TRUE")),
    grp,
    strata
  )
  n_ref <- colSums(t_tbl[1:2, 1, ])
  n_trt <- colSums(t_tbl[1:2, 2, ])
  use_stratum <- (n_ref > 0) & (n_trt > 0)
  n_ref <- n_ref[use_stratum]
  n_trt <- n_trt[use_stratum]
  p_ref <- t_tbl[2, 1, use_stratum] / n_ref
  p_trt <- t_tbl[2, 2, use_stratum] / n_trt
  est1 <- sum(weights * p_ref)
  est2 <- sum(weights * p_trt)
  diff_est <- est2 - est1

  lambda1 <- sum(weights^2 / n_ref)
  lambda2 <- sum(weights^2 / n_trt)
  z <- stats::qnorm((1 + conf_level) / 2)

  lower <- diff_est - z * sqrt(lambda2 * l_trt * (1 - l_trt) + lambda1 * u_ref * (1 - u_ref))
  upper <- diff_est + z * sqrt(lambda1 * l_ref * (1 - l_ref) + lambda2 * u_trt * (1 - u_trt))

  list(
    "diff" = diff_est,
    "diff_ci" = c("lower" = lower, "upper" = upper)
  )
}

#' Count specific values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_values()] creates a layout element to calculate counts of specific values within a
#' variable of interest.
#'
#' This function analyzes one or more variables of interest supplied as a vector to `vars`. Values to
#' count for variable(s) in `vars` can be given as a vector via the `values` argument. One row of
#' counts will be generated for each variable.
#'
#' @inheritParams argument_convention
#' @param values (`character`)\cr specific values that should be counted.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_values"), type = "sh")``
#'
#' @note
#' * For `factor` variables, `s_count_values` checks whether `values` are all included in the levels of `x`
#'   and fails otherwise.
#' * For `count_values()`, variable labels are shown when there is more than one element in `vars`,
#'   otherwise they are hidden.
#'
#' @name count_values
#' @order 1
NULL

#' @describeIn count_values S3 generic function to count values.
#'
#' @inheritParams s_summary.logical
#'
#' @return
#' * `s_count_values()` returns output of [s_summary()] for specified values of a non-numeric variable.
#'
#' @export
s_count_values <- function(x,
                           values,
                           na.rm = TRUE, # nolint
                           denom = c("n", "N_col", "N_row"),
                           ...) {
  UseMethod("s_count_values", x)
}

#' @describeIn count_values Method for `character` class.
#'
#' @method s_count_values character
#'
#' @examples
#' # `s_count_values.character`
#' s_count_values(x = c("a", "b", "a"), values = "a")
#' s_count_values(x = c("a", "b", "a", NA, NA), values = "b", na.rm = FALSE)
#'
#' @export
s_count_values.character <- function(x,
                                     values = "Y",
                                     na.rm = TRUE, # nolint
                                     ...) {
  checkmate::assert_character(values)

  if (na.rm) {
    x <- x[!is.na(x)]
  }

  is_in_values <- x %in% values

  s_summary(is_in_values, na_rm = na.rm, ...)
}

#' @describeIn count_values Method for `factor` class. This makes an automatic
#'   conversion to `character` and then forwards to the method for characters.
#'
#' @method s_count_values factor
#'
#' @examples
#' # `s_count_values.factor`
#' s_count_values(x = factor(c("a", "b", "a")), values = "a")
#'
#' @export
s_count_values.factor <- function(x,
                                  values = "Y",
                                  ...) {
  s_count_values(as.character(x), values = as.character(values), ...)
}

#' @describeIn count_values Method for `logical` class.
#'
#' @method s_count_values logical
#'
#' @examples
#' # `s_count_values.logical`
#' s_count_values(x = c(TRUE, FALSE, TRUE))
#'
#' @export
s_count_values.logical <- function(x, values = TRUE, ...) {
  checkmate::assert_logical(values)
  s_count_values(as.character(x), values = as.character(values), ...)
}

#' @describeIn count_values Formatted analysis function which is used as `afun`
#'   in `count_values()`.
#'
#' @return
#' * `a_count_values()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' # `a_count_values`
#' a_count_values(x = factor(c("a", "b", "a")), values = "a", .N_col = 10, .N_row = 10)
#'
#' @export
a_count_values <- function(x,
                           ...,
                           .stats = NULL,
                           .stat_names = NULL,
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = NULL) {
  dots_extra_args <- list(...)

  # Check for user-defined functions
  default_and_custom_stats_list <- .split_std_from_custom_stats(.stats)
  .stats <- default_and_custom_stats_list$default_stats
  custom_stat_functions <- default_and_custom_stats_list$custom_stats

  # Add extra parameters to the s_* function
  extra_afun_params <- retrieve_extra_afun_params(
    names(dots_extra_args$.additional_fun_parameters)
  )
  dots_extra_args$.additional_fun_parameters <- NULL

  # Main statistic calculations
  x_stats <- .apply_stat_functions(
    default_stat_fnc = s_count_values,
    custom_stat_fnc_list = custom_stat_functions,
    args_list = c(
      x = list(x),
      extra_afun_params,
      dots_extra_args
    )
  )

  # Fill in formatting defaults
  .stats <- c(
    get_stats("analyze_vars_counts", stats_in = .stats),
    names(custom_stat_functions) # Additional stats from custom functions
  )
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  x_stats <- x_stats[.stats]

  # Auto format handling
  .formats <- apply_auto_formatting(
    .formats,
    x_stats,
    extra_afun_params$.df_row,
    extra_afun_params$.var
  )

  # Get and check statistic names from defaults
  .stat_names <- get_stat_names(x_stats, .stat_names)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = names(.labels),
    .stat_names = .stat_names,
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls()
  )
}

#' @describeIn count_values Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_values()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_values()` to the table layout.
#'
#' @examples
#' # `count_values`
#' basic_table() %>%
#'   count_values("Species", values = "setosa") %>%
#'   build_table(iris)
#'
#' @export
#' @order 2
count_values <- function(lyt,
                         vars,
                         values,
                         na_str = default_na_str(),
                         na_rm = TRUE,
                         nested = TRUE,
                         ...,
                         table_names = vars,
                         .stats = "count_fraction",
                         .stat_names = NULL,
                         .formats = c(count_fraction = "xx (xx.xx%)", count = "xx"),
                         .labels = c(count_fraction = paste(values, collapse = ", ")),
                         .indent_mods = NULL) {
  # Process extra args
  extra_args <- list("na_rm" = na_rm, "values" = values, ...)
  if (!is.null(.stats)) extra_args[[".stats"]] <- .stats
  if (!is.null(.stat_names)) extra_args[[".stat_names"]] <- .stat_names
  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  # Adding additional info from layout to analysis function
  extra_args[[".additional_fun_parameters"]] <- get_additional_afun_params(add_alt_df = FALSE)
  formals(a_count_values) <- c(formals(a_count_values), extra_args[[".additional_fun_parameters"]])

  analyze(
    lyt,
    vars,
    afun = a_count_values,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = ifelse(length(vars) > 1, "visible", "hidden"),
    table_names = table_names
  )
}

#' Count occurrences by grade
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_occurrences_by_grade()] creates a layout element to calculate occurrence counts by grade.
#'
#' This function analyzes primary analysis variable `var` which indicates toxicity grades. The `id` variable
#' is used to indicate unique subject identifiers (defaults to `USUBJID`). The user can also supply a list of
#' custom groups of grades to analyze via the `grade_groups` parameter. The `remove_single`  argument will
#' remove single grades from the analysis so that *only* grade groups are analyzed.
#'
#' If there are multiple grades recorded for one patient only the highest grade level is counted.
#'
#' The summarize function [summarize_occurrences_by_grade()] performs the same function as
#' [count_occurrences_by_grade()] except it creates content rows, not data rows, to summarize the current table
#' row/column context and operates on the level of the latest row split or the root of the table if no row splits have
#' occurred.
#'
#' @inheritParams count_occurrences
#' @inheritParams argument_convention
#' @param grade_groups (named `list` of `character`)\cr list containing groupings of grades.
#' @param remove_single (`flag`)\cr `TRUE` to not include the elements of one-element grade groups
#'   in the the output list; in this case only the grade groups names will be included in the output. If
#'   `only_grade_groups` is set to `TRUE` this argument is ignored.
#' @param only_grade_groups (`flag`)\cr whether only the specified grade groups should be
#'   included, with individual grade rows removed (`TRUE`), or all grades and grade groups
#'   should be displayed (`FALSE`).
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_occurrences_by_grade"), type = "sh")``
#'
#' @seealso Relevant helper function [h_append_grade_groups()].
#'
#' @name count_occurrences_by_grade
#' @order 1
NULL

#' Helper function for `s_count_occurrences_by_grade()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function for [s_count_occurrences_by_grade()] to insert grade groupings into list with
#' individual grade frequencies. The order of the final result follows the order of `grade_groups`.
#' The elements under any-grade group (if any), i.e. the grade group equal to `refs` will be moved to
#' the end. Grade groups names must be unique.
#'
#' @inheritParams count_occurrences_by_grade
#' @param refs (named `list` of `numeric`)\cr named list where each name corresponds to a reference grade level
#'   and each entry represents a count.
#'
#' @return Formatted list of grade groupings.
#'
#' @examples
#' h_append_grade_groups(
#'   list(
#'     "Any Grade" = as.character(1:5),
#'     "Grade 1-2" = c("1", "2"),
#'     "Grade 3-4" = c("3", "4")
#'   ),
#'   list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)
#' )
#'
#' h_append_grade_groups(
#'   list(
#'     "Any Grade" = as.character(5:1),
#'     "Grade A" = "5",
#'     "Grade B" = c("4", "3")
#'   ),
#'   list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)
#' )
#'
#' h_append_grade_groups(
#'   list(
#'     "Any Grade" = as.character(1:5),
#'     "Grade 1-2" = c("1", "2"),
#'     "Grade 3-4" = c("3", "4")
#'   ),
#'   list("1" = 10, "2" = 5, "3" = 0)
#' )
#'
#' @export
h_append_grade_groups <- function(grade_groups, refs, remove_single = TRUE, only_grade_groups = FALSE) {
  checkmate::assert_list(grade_groups)
  checkmate::assert_list(refs)
  refs_orig <- refs
  elements <- unique(unlist(grade_groups))

  ### compute sums in groups
  grp_sum <- lapply(grade_groups, function(i) do.call(sum, refs[i]))
  if (!checkmate::test_subset(elements, names(refs))) {
    padding_el <- setdiff(elements, names(refs))
    refs[padding_el] <- 0
  }
  result <- c(grp_sum, refs)

  ### order result while keeping grade_groups's ordering
  ordr <- grade_groups

  # elements of any-grade group (if any) will be moved to the end
  is_any <- sapply(grade_groups, setequal, y = names(refs))
  ordr[is_any] <- list(character(0)) # hide elements under any-grade group

  # groups-elements combined sequence
  ordr <- c(lapply(names(ordr), function(g) c(g, ordr[[g]])), recursive = TRUE, use.names = FALSE)
  ordr <- ordr[!duplicated(ordr)]

  # append remaining elements (if any)
  ordr <- union(ordr, unlist(grade_groups[is_any])) # from any-grade group
  ordr <- union(ordr, names(refs)) # from refs

  # remove elements of single-element groups, if any
  if (only_grade_groups) {
    ordr <- intersect(ordr, names(grade_groups))
  } else if (remove_single) {
    is_single <- sapply(grade_groups, length) == 1L
    ordr <- setdiff(ordr, unlist(grade_groups[is_single]))
  }

  # apply the order
  result <- result[ordr]

  # remove groups without any elements in the original refs
  # note: it's OK if groups have 0 value
  keep_grp <- vapply(grade_groups, function(x, rf) {
    any(x %in% rf)
  }, rf = names(refs_orig), logical(1))

  keep_el <- names(result) %in% names(refs_orig) | names(result) %in% names(keep_grp)[keep_grp]
  result <- result[keep_el]

  result
}

#' @describeIn count_occurrences_by_grade Statistics function which counts the
#'  number of patients by highest grade.
#'
#' @return
#' * `s_count_occurrences_by_grade()` returns a list of counts and fractions with one element per grade level or
#'   grade level grouping.
#'
#' @examples
#' s_count_occurrences_by_grade(
#'   df,
#'   .N_col = 10L,
#'   .var = "AETOXGR",
#'   id = "USUBJID",
#'   grade_groups = list("ANY" = levels(df$AETOXGR))
#' )
#'
#' @export
s_count_occurrences_by_grade <- function(df,
                                         .var,
                                         .N_row, # nolint
                                         .N_col, # nolint
                                         id = "USUBJID",
                                         grade_groups = list(),
                                         remove_single = TRUE,
                                         only_grade_groups = FALSE,
                                         denom = c("N_col", "n", "N_row"),
                                         labelstr = "") {
  assert_valid_factor(df[[.var]])
  assert_df_with_variables(df, list(grade = .var, id = id))

  denom <- match.arg(denom) %>%
    switch(
      n = nlevels(factor(df[[id]])),
      N_row = .N_row,
      N_col = .N_col
    )

  if (nrow(df) < 1) {
    grade_levels <- levels(df[[.var]])
    l_count <- as.list(rep(0, length(grade_levels)))
    names(l_count) <- grade_levels
  } else {
    if (isTRUE(is.factor(df[[id]]))) {
      assert_valid_factor(df[[id]], any.missing = FALSE)
    } else {
      checkmate::assert_character(df[[id]], min.chars = 1, any.missing = FALSE)
    }
    checkmate::assert_count(.N_col)

    id <- df[[id]]
    grade <- df[[.var]]

    if (!is.ordered(grade)) {
      grade_lbl <- obj_label(grade)
      lvls <- levels(grade)
      if (sum(grepl("^\\d+$", lvls)) %in% c(0, length(lvls))) {
        lvl_ord <- lvls
      } else {
        lvls[!grepl("^\\d+$", lvls)] <- min(as.numeric(lvls[grepl("^\\d+$", lvls)])) - 1
        lvl_ord <- levels(grade)[order(as.numeric(lvls))]
      }
      grade <- formatters::with_label(factor(grade, levels = lvl_ord, ordered = TRUE), grade_lbl)
    }

    missing_lvl <- grepl("missing", tolower(levels(grade)))
    if (any(missing_lvl)) {
      grade <- factor(
        grade,
        levels = c(levels(grade)[!missing_lvl], levels(grade)[missing_lvl]),
        ordered = is.ordered(grade)
      )
    }
    df_max <- stats::aggregate(grade ~ id, FUN = max, drop = FALSE)
    l_count <- as.list(table(df_max$grade))
  }

  if (length(grade_groups) > 0) {
    l_count <- h_append_grade_groups(grade_groups, l_count, remove_single, only_grade_groups)
  }

  l_count_fraction <- lapply(
    l_count,
    function(i, denom) {
      if (i == 0 && denom == 0) {
        c(0, 0)
      } else {
        c(i, i / denom)
      }
    },
    denom = denom
  )

  list(
    count_fraction = l_count_fraction,
    count_fraction_fixed_dp = l_count_fraction
  )
}

#' @describeIn count_occurrences_by_grade Formatted analysis function which is used as `afun`
#'   in `count_occurrences_by_grade()`.
#'
#' @return
#' * `a_count_occurrences_by_grade()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_occurrences_by_grade(
#'   df,
#'   .N_col = 10L,
#'   .N_row = 10L,
#'   .var = "AETOXGR",
#'   id = "USUBJID",
#'   grade_groups = list("ANY" = levels(df$AETOXGR))
#' )
#'
#' @export
a_count_occurrences_by_grade <- function(df,
                                         labelstr = "",
                                         id = "USUBJID",
                                         grade_groups = list(),
                                         remove_single = TRUE,
                                         only_grade_groups = FALSE,
                                         denom = c("N_col", "n", "N_row"),
                                         .N_col, # nolint
                                         .N_row, # nolint
                                         .df_row,
                                         .var = NULL,
                                         .stats = NULL,
                                         .formats = NULL,
                                         .labels = NULL,
                                         .indent_mods = NULL,
                                         na_str = default_na_str()) {
  x_stats <- s_count_occurrences_by_grade(
    df = df, .var = .var, .N_row = .N_row, .N_col = .N_col, id = id,
    grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups,
    denom = denom, labelstr = labelstr
  )

  if (is.null(unlist(x_stats))) {
    return(NULL)
  }

  # Fill in with formatting defaults if needed
  .stats <- get_stats("count_occurrences_by_grade", stats_in = .stats)
  x_stats <- x_stats[.stats]
  levels_per_stats <- lapply(x_stats, names)
  .formats <- get_formats_from_stats(.stats, .formats, levels_per_stats)
  .labels <- get_labels_from_stats(.stats, .labels, levels_per_stats)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods, levels_per_stats)

  # Unlist stats
  x_stats <- x_stats %>% .unlist_keep_nulls()

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = .labels %>% .unlist_keep_nulls(),
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls(),
    .format_na_strs = na_str
  )
}

#' @describeIn count_occurrences_by_grade Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_occurrences_by_grade()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(1:6, 1)),
#'   ARM = factor(c("A", "A", "A", "B", "B", "B", "A"), levels = c("A", "B")),
#'   AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),
#'   AESEV = factor(
#'     x = c("MILD", "MODERATE", "SEVERE", "MILD", "MILD", "MODERATE", "SEVERE"),
#'     levels = c("MILD", "MODERATE", "SEVERE")
#'   ),
#'   stringsAsFactors = FALSE
#' )
#'
#' df_adsl <- df %>%
#'   select(USUBJID, ARM) %>%
#'   unique()
#'
#' # Layout creating function with custom format.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_occurrences_by_grade(
#'     var = "AESEV",
#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' # Define additional grade groupings.
#' grade_groups <- list(
#'   "-Any-" = c("1", "2", "3", "4", "5"),
#'   "Grade 1-2" = c("1", "2"),
#'   "Grade 3-5" = c("3", "4", "5")
#' )
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_occurrences_by_grade(
#'     var = "AETOXGR",
#'     grade_groups = grade_groups,
#'     only_grade_groups = TRUE
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' @export
#' @order 2
count_occurrences_by_grade <- function(lyt,
                                       var,
                                       id = "USUBJID",
                                       grade_groups = list(),
                                       remove_single = TRUE,
                                       only_grade_groups = FALSE,
                                       var_labels = var,
                                       show_labels = "default",
                                       riskdiff = FALSE,
                                       na_str = default_na_str(),
                                       nested = TRUE,
                                       ...,
                                       table_names = var,
                                       .stats = "count_fraction",
                                       .formats = list(count_fraction = format_count_fraction_fixed_dp),
                                       .indent_mods = NULL,
                                       .labels = NULL) {
  checkmate::assert_flag(riskdiff)
  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(
    id = id, grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups, ...
  )

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_occurrences_by_grade" = a_count_occurrences_by_grade),
        s_args = s_args
      )
    )
  }

  analyze(
    lyt = lyt,
    vars = var,
    afun = ifelse(isFALSE(riskdiff), a_count_occurrences_by_grade, afun_riskdiff),
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' @describeIn count_occurrences_by_grade Layout-creating function which can take content function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @return
#' * `summarize_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
#'   containing the statistics from `s_count_occurrences_by_grade()` to the table layout.
#'
#' @examples
#' # Layout creating function with custom format.
#' basic_table() %>%
#'   add_colcounts() %>%
#'   split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
#'   summarize_occurrences_by_grade(
#'     var = "AESEV",
#'     .formats = c("count_fraction" = "xx.xx (xx.xx%)")
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' basic_table() %>%
#'   add_colcounts() %>%
#'   split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
#'   summarize_occurrences_by_grade(
#'     var = "AETOXGR",
#'     grade_groups = grade_groups
#'   ) %>%
#'   build_table(df, alt_counts_df = df_adsl)
#'
#' @export
#' @order 3
summarize_occurrences_by_grade <- function(lyt,
                                           var,
                                           id = "USUBJID",
                                           grade_groups = list(),
                                           remove_single = TRUE,
                                           only_grade_groups = FALSE,
                                           riskdiff = FALSE,
                                           na_str = default_na_str(),
                                           ...,
                                           .stats = "count_fraction",
                                           .formats = list(count_fraction = format_count_fraction_fixed_dp),
                                           .indent_mods = NULL,
                                           .labels = NULL) {
  checkmate::assert_flag(riskdiff)
  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(
    id = id, grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups, ...
  )

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_occurrences_by_grade" = a_count_occurrences_by_grade),
        s_args = s_args
      )
    )
  }

  summarize_row_groups(
    lyt = lyt,
    var = var,
    cfun = ifelse(isFALSE(riskdiff), a_count_occurrences_by_grade, afun_riskdiff),
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Encode categorical missing values in a data frame
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a helper function to encode missing entries across groups of categorical
#' variables in a data frame.
#'
#' @details Missing entries are those with `NA` or empty strings and will
#'   be replaced with a specified value. If factor variables include missing
#'   values, the missing value will be inserted as the last level.
#'   Similarly, in case character or logical variables should be converted to factors
#'   with the `char_as_factor` or `logical_as_factor` options, the missing values will
#'   be set as the last level.
#'
#' @param data (`data.frame`)\cr data set.
#' @param omit_columns (`character`)\cr names of variables from `data` that should
#'   not be modified by this function.
#' @param char_as_factor (`flag`)\cr whether to convert character variables
#'   in `data` to factors.
#' @param logical_as_factor (`flag`)\cr whether to convert logical variables
#'   in `data` to factors.
#' @param na_level (`string`)\cr string used to replace all `NA` or empty
#'   values inside non-`omit_columns` columns.
#'
#' @return A `data.frame` with the chosen modifications applied.
#'
#' @seealso [sas_na()] and [explicit_na()] for other missing data helper functions.
#'
#' @examples
#' my_data <- data.frame(
#'   u = c(TRUE, FALSE, NA, TRUE),
#'   v = factor(c("A", NA, NA, NA), levels = c("Z", "A")),
#'   w = c("A", "B", NA, "C"),
#'   x = c("D", "E", "F", NA),
#'   y = c("G", "H", "I", ""),
#'   z = c(1, 2, 3, 4),
#'   stringsAsFactors = FALSE
#' )
#'
#' # Example 1
#' # Encode missing values in all character or factor columns.
#' df_explicit_na(my_data)
#' # Also convert logical columns to factor columns.
#' df_explicit_na(my_data, logical_as_factor = TRUE)
#' # Encode missing values in a subset of columns.
#' df_explicit_na(my_data, omit_columns = c("x", "y"))
#'
#' # Example 2
#' # Here we purposefully convert all `M` values to `NA` in the `SEX` variable.
#' # After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not
#' # included when generating `rtables`.
#' adsl <- tern_ex_adsl
#' adsl$SEX[adsl$SEX == "M"] <- NA
#' adsl <- df_explicit_na(adsl)
#'
#' # If you want the `Na` values to be displayed in the table use the `na_level` argument.
#' adsl <- tern_ex_adsl
#' adsl$SEX[adsl$SEX == "M"] <- NA
#' adsl <- df_explicit_na(adsl, na_level = "Missing Values")
#'
#' # Example 3
#' # Numeric variables that have missing values are not altered. This means that any `NA` value in
#' # a numeric variable will not be included in the summary statistics, nor will they be included
#' # in the denominator value for calculating the percent values.
#' adsl <- tern_ex_adsl
#' adsl$AGE[adsl$AGE < 30] <- NA
#' adsl <- df_explicit_na(adsl)
#'
#' @export
df_explicit_na <- function(data,
                           omit_columns = NULL,
                           char_as_factor = TRUE,
                           logical_as_factor = FALSE,
                           na_level = "<Missing>") {
  checkmate::assert_character(omit_columns, null.ok = TRUE, min.len = 1, any.missing = FALSE)
  checkmate::assert_data_frame(data)
  checkmate::assert_flag(char_as_factor)
  checkmate::assert_flag(logical_as_factor)
  checkmate::assert_string(na_level)

  target_vars <- if (is.null(omit_columns)) {
    names(data)
  } else {
    setdiff(names(data), omit_columns) # May have duplicates.
  }
  if (length(target_vars) == 0) {
    return(data)
  }

  l_target_vars <- split(target_vars, target_vars)

  # Makes sure target_vars exist in data and names are not duplicated.
  assert_df_with_variables(data, l_target_vars)

  for (x in target_vars) {
    xi <- data[[x]]
    xi_label <- obj_label(xi)

    # Determine whether to convert character or logical input.
    do_char_conversion <- is.character(xi) && char_as_factor
    do_logical_conversion <- is.logical(xi) && logical_as_factor

    # Pre-convert logical to character to deal correctly with replacing NA
    # values below.
    if (do_logical_conversion) {
      xi <- as.character(xi)
    }

    if (is.factor(xi) || is.character(xi)) {
      # Handle empty strings and NA values.
      xi <- explicit_na(sas_na(xi), label = na_level)

      # Convert to factors if requested for the original type,
      # set na_level as the last value.
      if (do_char_conversion || do_logical_conversion) {
        levels_xi <- setdiff(sort(unique(xi)), na_level)
        if (na_level %in% unique(xi)) {
          levels_xi <- c(levels_xi, na_level)
        }

        xi <- factor(xi, levels = levels_xi)
      }

      data[, x] <- formatters::with_label(xi, label = xi_label)
    }
  }
  return(data)
}

#' Helper function for deriving analysis datasets for select laboratory tables
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function that merges ADSL and ADLB datasets so that missing lab test records are inserted in the
#' output dataset. Remember that `na_level` must match the needed pre-processing
#' done with [df_explicit_na()] to have the desired output.
#'
#' @param adsl (`data.frame`)\cr ADSL data frame.
#' @param adlb (`data.frame`)\cr ADLB data frame.
#' @param worst_flag (named `character`)\cr worst post-baseline lab flag variable. See how this is implemented in the
#'   following examples.
#' @param by_visit (`flag`)\cr defaults to `FALSE` to generate worst grade per patient.
#'   If worst grade per patient per visit is specified for `worst_flag`, then
#'   `by_visit` should be `TRUE` to generate worst grade patient per visit.
#' @param no_fillin_visits (named `character`)\cr visits that are not considered for post-baseline worst toxicity
#'   grade. Defaults to `c("SCREENING", "BASELINE")`.
#'
#' @return `df` containing variables shared between `adlb` and `adsl` along with variables `PARAM`, `PARAMCD`,
#'   `ATOXGR`, and `BTOXGR` relevant for analysis. Optionally, `AVISIT` are `AVISITN` are included when
#'   `by_visit = TRUE` and `no_fillin_visits = c("SCREENING", "BASELINE")`.
#'
#' @details In the result data missing records will be created for the following situations:
#'   * Patients who are present in `adsl` but have no lab data in `adlb` (both baseline and post-baseline).
#'   * Patients who do not have any post-baseline lab values.
#'   * Patients without any post-baseline values flagged as the worst.
#'
#' @examples
#' # `h_adsl_adlb_merge_using_worst_flag`
#' adlb_out <- h_adsl_adlb_merge_using_worst_flag(
#'   tern_ex_adsl,
#'   tern_ex_adlb,
#'   worst_flag = c("WGRHIFL" = "Y")
#' )
#'
#' # `h_adsl_adlb_merge_using_worst_flag` by visit example
#' adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag(
#'   tern_ex_adsl,
#'   tern_ex_adlb,
#'   worst_flag = c("WGRLOVFL" = "Y"),
#'   by_visit = TRUE
#' )
#'
#' @export
h_adsl_adlb_merge_using_worst_flag <- function(adsl, # nolint
                                               adlb,
                                               worst_flag = c("WGRHIFL" = "Y"),
                                               by_visit = FALSE,
                                               no_fillin_visits = c("SCREENING", "BASELINE")) {
  col_names <- names(worst_flag)
  filter_values <- worst_flag

  temp <- Map(
    function(x, y) which(adlb[[x]] == y),
    col_names,
    filter_values
  )

  position_satisfy_filters <- Reduce(intersect, temp)

  adsl_adlb_common_columns <- intersect(colnames(adsl), colnames(adlb))
  columns_from_adlb <- c("USUBJID", "PARAM", "PARAMCD", "AVISIT", "AVISITN", "ATOXGR", "BTOXGR")

  adlb_f <- adlb[position_satisfy_filters, ] %>%
    dplyr::filter(!.data[["AVISIT"]] %in% no_fillin_visits)
  adlb_f <- adlb_f[, columns_from_adlb]

  avisits_grid <- adlb %>%
    dplyr::filter(!.data[["AVISIT"]] %in% no_fillin_visits) %>%
    dplyr::pull(.data[["AVISIT"]]) %>%
    unique()

  if (by_visit) {
    adsl_lb <- expand.grid(
      USUBJID = unique(adsl$USUBJID),
      AVISIT = avisits_grid,
      PARAMCD = unique(adlb$PARAMCD)
    )

    adsl_lb <- adsl_lb %>%
      dplyr::left_join(unique(adlb[c("AVISIT", "AVISITN")]), by = "AVISIT") %>%
      dplyr::left_join(unique(adlb[c("PARAM", "PARAMCD")]), by = "PARAMCD")

    adsl1 <- adsl[, adsl_adlb_common_columns]
    adsl_lb <- adsl1 %>% merge(adsl_lb, by = "USUBJID")

    by_variables_from_adlb <- c("USUBJID", "AVISIT", "AVISITN", "PARAMCD", "PARAM")

    adlb_btoxgr <- adlb %>%
      dplyr::select(c("USUBJID", "PARAMCD", "BTOXGR")) %>%
      unique() %>%
      dplyr::rename("BTOXGR_MAP" = "BTOXGR")

    adlb_out <- merge(
      adlb_f,
      adsl_lb,
      by = by_variables_from_adlb,
      all = TRUE,
      sort = FALSE
    )
    adlb_out <- adlb_out %>%
      dplyr::left_join(adlb_btoxgr, by = c("USUBJID", "PARAMCD")) %>%
      dplyr::mutate(BTOXGR = .data$BTOXGR_MAP) %>%
      dplyr::select(-"BTOXGR_MAP")

    adlb_var_labels <- c(
      formatters::var_labels(adlb[by_variables_from_adlb]),
      formatters::var_labels(adlb[columns_from_adlb[!columns_from_adlb %in% by_variables_from_adlb]]),
      formatters::var_labels(adsl[adsl_adlb_common_columns[adsl_adlb_common_columns != "USUBJID"]])
    )
  } else {
    adsl_lb <- expand.grid(
      USUBJID = unique(adsl$USUBJID),
      PARAMCD = unique(adlb$PARAMCD)
    )

    adsl_lb <- adsl_lb %>% dplyr::left_join(unique(adlb[c("PARAM", "PARAMCD")]), by = "PARAMCD")

    adsl1 <- adsl[, adsl_adlb_common_columns]
    adsl_lb <- adsl1 %>% merge(adsl_lb, by = "USUBJID")

    by_variables_from_adlb <- c("USUBJID", "PARAMCD", "PARAM")

    adlb_out <- merge(
      adlb_f,
      adsl_lb,
      by = by_variables_from_adlb,
      all = TRUE,
      sort = FALSE
    )

    adlb_var_labels <- c(
      formatters::var_labels(adlb[by_variables_from_adlb]),
      formatters::var_labels(adlb[columns_from_adlb[!columns_from_adlb %in% by_variables_from_adlb]]),
      formatters::var_labels(adsl[adsl_adlb_common_columns[adsl_adlb_common_columns != "USUBJID"]])
    )
  }

  adlb_out$ATOXGR <- as.factor(adlb_out$ATOXGR)
  adlb_out$BTOXGR <- as.factor(adlb_out$BTOXGR)

  formatters::var_labels(adlb_out) <- adlb_var_labels

  adlb_out
}

#' Tabulate survival duration by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The [tabulate_survival_subgroups()] function creates a layout element to tabulate survival duration by subgroup,
#' returning statistics including median survival time and hazard ratio for each population subgroup. The table is
#' created from `df`, a list of data frames returned by [extract_survival_subgroups()], with the statistics to include
#' specified via the `vars` parameter.
#'
#' A forest plot can be created from the resulting table using the [g_forest()] function.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_coxph_pairwise
#' @param df (`list`)\cr list of data frames containing all analysis variables. List should be
#'   created using [extract_survival_subgroups()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n_tot_events`: Total number of events per group.
#'   * `n_events`: Number of events per group.
#'   * `n_tot`: Total number of observations per group.
#'   * `n`: Number of observations per group.
#'   * `median`: Median survival time.
#'   * `hr`: Hazard ratio.
#'   * `ci`: Confidence interval of hazard ratio.
#'   * `pval`: p-value of the effect.
#'   Note, one of the statistics `n_tot` and `n_tot_events`, as well as both `hr` and `ci`
#'   are required.
#' @param time_unit (`string`)\cr label with unit of median survival time. Default `NULL` skips displaying unit.
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. Tables typically used as part of forest plot.
#'
#' @seealso [extract_survival_subgroups()]
#'
#' @examples
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte)
#'
#' adtte_f <- adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X"),
#'     SEX %in% c("M", "F")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
#'     SEX = droplevels(SEX),
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- c(
#'   "ARM" = adtte_labels[["ARM"]],
#'   "SEX" = adtte_labels[["SEX"]],
#'   "AVALU" = adtte_labels[["AVALU"]],
#'   "is_event" = "Event Flag"
#' )
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' df <- extract_survival_subgroups(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM", subgroups = c("SEX", "BMRKR2")
#'   ),
#'   label_all = "Total Patients",
#'   data = adtte_f
#' )
#' df
#'
#' df_grouped <- extract_survival_subgroups(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM", subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#' df_grouped
#'
#' @name survival_duration_subgroups
#' @order 1
NULL

#' Prepare survival data for population subgroups in data frames
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares estimates of median survival times and treatment hazard ratios for population subgroups in
#' data frames. Simple wrapper for [h_survtime_subgroups_df()] and [h_coxph_subgroups_df()]. Result is a `list`
#' of two `data.frame`s: `survtime` and `hr`. `variables` corresponds to the names of variables found in `data`,
#' passed as a named `list` and requires elements `tte`, `is_event`, `arm` and optionally `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_duration_subgroups
#' @inheritParams survival_coxph_pairwise
#'
#' @return A named `list` of two elements:
#'   * `survtime`: A `data.frame` containing columns `arm`, `n`, `n_events`, `median`, `subgroup`, `var`,
#'     `var_label`, and `row_type`.
#'   * `hr`: A `data.frame` containing columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`, `conf_level`,
#'     `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @seealso [survival_duration_subgroups]
#'
#' @export
extract_survival_subgroups <- function(variables,
                                       data,
                                       groups_lists = list(),
                                       control = control_coxph(),
                                       label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_survival_subgroups() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  df_survtime <- h_survtime_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    label_all = label_all
  )
  df_hr <- h_coxph_subgroups_df(
    variables,
    data,
    groups_lists = groups_lists,
    control = control,
    label_all = label_all
  )

  list(survtime = df_survtime, hr = df_hr)
}

#' @describeIn survival_duration_subgroups  Formatted analysis function which is used as
#'   `afun` in `tabulate_survival_subgroups()`.
#'
#' @return
#' * `a_survival_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_survival_subgroups <- function(.formats = list( # nolint start
                                   n = "xx",
                                   n_events = "xx",
                                   n_tot_events = "xx",
                                   median = "xx.x",
                                   n_tot = "xx",
                                   hr = list(format_extreme_values(2L)),
                                   ci = list(format_extreme_values_ci(2L)),
                                   pval = "x.xxxx | (<0.0001)"
                                 ),
                                 na_str = default_na_str()) { # nolint end
  checkmate::assert_list(.formats)
  checkmate::assert_subset(
    names(.formats),
    c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval", "riskdiff")
  )

  afun_lst <- Map(
    function(stat, fmt, na_str) {
      function(df, labelstr = "", ...) {
        in_rows(
          .list = as.list(df[[stat]]),
          .labels = as.character(df$subgroup),
          .formats = fmt,
          .format_na_strs = na_str
        )
      }
    },
    stat = names(.formats),
    fmt = .formats,
    na_str = na_str
  )

  afun_lst
}

#' @describeIn survival_duration_subgroups Table-creating function which creates a table
#'   summarizing survival by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
#'   and [rtables::summarize_row_groups()].
#'
#' @param label_all `r lifecycle::badge("deprecated")`\cr please assign the `label_all` parameter within the
#'   [extract_survival_subgroups()] function when creating `df`.
#' @param riskdiff (`list`)\cr if a risk (proportion) difference column should be added, a list of settings to apply
#'   within the column. See [control_riskdiff()] for details. If `NULL`, no risk difference column will be added. If
#'   `riskdiff$arm_x` and `riskdiff$arm_y` are `NULL`, the first level of `df$survtime$arm` will be used as `arm_x`
#'   and the second level as `arm_y`.
#'
#' @return An `rtables` table summarizing survival by subgroup.
#'
#' @examples
#' ## Table with default columns.
#' basic_table() %>%
#'   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
#'
#' ## Table with a manually chosen set of columns: adding "pval".
#' basic_table() %>%
#'   tabulate_survival_subgroups(
#'     df = df,
#'     vars = c("n_tot_events", "n_events", "median", "hr", "ci", "pval"),
#'     time_unit = adtte_f$AVALU[1]
#'   )
#'
#' @export
#' @order 2
tabulate_survival_subgroups <- function(lyt,
                                        df,
                                        vars = c("n_tot_events", "n_events", "median", "hr", "ci"),
                                        groups_lists = list(),
                                        label_all = lifecycle::deprecated(),
                                        time_unit = NULL,
                                        riskdiff = NULL,
                                        na_str = default_na_str(),
                                        .formats = c(
                                          n = "xx", n_events = "xx", n_tot_events = "xx", median = "xx.x", n_tot = "xx",
                                          hr = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)),
                                          pval = "x.xxxx | (<0.0001)"
                                        )) {
  checkmate::assert_list(riskdiff, null.ok = TRUE)
  checkmate::assert_true(any(c("n_tot", "n_tot_events") %in% vars))
  checkmate::assert_true(all(c("hr", "ci") %in% vars))

  if (lifecycle::is_present(label_all)) {
    lifecycle::deprecate_warn(
      "0.9.5", "tabulate_survival_subgroups(label_all)",
      details =
        "Please assign the `label_all` parameter within the `extract_survival_subgroups()` function when creating `df`."
    )
  }

  # Create "ci" column from "lcl" and "ucl"
  df$hr$ci <- combine_vectors(df$hr$lcl, df$hr$ucl)

  # Fill in missing formats with defaults
  default_fmts <- eval(formals(tabulate_survival_subgroups)$.formats)
  .formats <- c(.formats, default_fmts[vars[!vars %in% names(.formats)]])

  # Extract additional parameters from df
  conf_level <- df$hr$conf_level[1]
  method <- df$hr$pval_label[1]
  colvars <- d_survival_subgroups_colvars(vars, conf_level = conf_level, method = method, time_unit = time_unit)
  survtime_vars <- intersect(colvars$vars, c("n", "n_events", "median"))
  hr_vars <- intersect(names(colvars$labels), c("n_tot", "n_tot_events", "hr", "ci", "pval"))
  colvars_survtime <- list(vars = survtime_vars, labels = colvars$labels[survtime_vars])
  colvars_hr <- list(vars = hr_vars, labels = colvars$labels[hr_vars])

  extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method)

  # Get analysis function for each statistic
  afun_lst <- a_survival_subgroups(.formats = c(.formats, riskdiff = riskdiff$format), na_str = na_str)

  # Add risk difference column
  if (!is.null(riskdiff)) {
    if (is.null(riskdiff$arm_x)) riskdiff$arm_x <- levels(df$survtime$arm)[1]
    if (is.null(riskdiff$arm_y)) riskdiff$arm_y <- levels(df$survtime$arm)[2]
    colvars_hr$vars <- c(colvars_hr$vars, "riskdiff")
    colvars_hr$labels <- c(colvars_hr$labels, riskdiff = riskdiff$col_label)
    arm_cols <- paste(rep(c("n_events", "n_events", "n", "n")), c(riskdiff$arm_x, riskdiff$arm_y), sep = "_")

    df_prop_diff <- df$survtime %>%
      dplyr::select(-"median") %>%
      tidyr::pivot_wider(
        id_cols = c("subgroup", "var", "var_label", "row_type"),
        names_from = "arm",
        values_from = c("n", "n_events")
      ) %>%
      dplyr::rowwise() %>%
      dplyr::mutate(
        riskdiff = stat_propdiff_ci(
          x = as.list(.data[[arm_cols[1]]]),
          y = as.list(.data[[arm_cols[2]]]),
          N_x = .data[[arm_cols[3]]],
          N_y = .data[[arm_cols[4]]]
        )
      ) %>%
      dplyr::select(-dplyr::all_of(arm_cols))

    df$hr <- df$hr %>%
      dplyr::left_join(
        df_prop_diff,
        by = c("subgroup", "var", "var_label", "row_type")
      )
  }

  # Add columns from table_survtime (optional)
  if (length(colvars_survtime$vars) > 0) {
    lyt_survtime <- split_cols_by(lyt = lyt, var = "arm")
    lyt_survtime <- split_rows_by(
      lyt = lyt_survtime,
      var = "row_type",
      split_fun = keep_split_levels("content"),
      nested = FALSE
    )

    # Add "All Patients" row
    lyt_survtime <- summarize_row_groups(
      lyt = lyt_survtime,
      var = "var_label",
      cfun = afun_lst[names(colvars_survtime$labels)],
      na_str = na_str,
      extra_args = extra_args
    )
    lyt_survtime <- split_cols_by_multivar(
      lyt = lyt_survtime,
      vars = colvars_survtime$vars,
      varlabels = colvars_survtime$labels
    )

    # Add analysis rows
    if ("analysis" %in% df$survtime$row_type) {
      lyt_survtime <- split_rows_by(
        lyt = lyt_survtime,
        var = "row_type",
        split_fun = keep_split_levels("analysis"),
        nested = FALSE,
        child_labels = "hidden"
      )
      lyt_survtime <- split_rows_by(lyt = lyt_survtime, var = "var_label", nested = TRUE)
      lyt_survtime <- analyze_colvars(
        lyt = lyt_survtime,
        afun = afun_lst[names(colvars_survtime$labels)],
        na_str = na_str,
        inclNAs = TRUE,
        extra_args = extra_args
      )
    }

    table_survtime <- build_table(lyt_survtime, df = df$survtime)
  } else {
    table_survtime <- NULL
  }

  # Add columns from table_hr ("n_tot_events" or "n_tot", "or" and "ci" required)
  lyt_hr <- split_cols_by(lyt = lyt, var = "arm")
  lyt_hr <- split_rows_by(
    lyt = lyt_hr,
    var = "row_type",
    split_fun = keep_split_levels("content"),
    nested = FALSE
  )
  lyt_hr <- summarize_row_groups(
    lyt = lyt_hr,
    var = "var_label",
    cfun = afun_lst[names(colvars_hr$labels)],
    na_str = na_str,
    extra_args = extra_args
  )
  lyt_hr <- split_cols_by_multivar(
    lyt = lyt_hr,
    vars = colvars_hr$vars,
    varlabels = colvars_hr$labels
  ) %>%
    append_topleft("Baseline Risk Factors")

  # Add analysis rows
  if ("analysis" %in% df$survtime$row_type) {
    lyt_hr <- split_rows_by(
      lyt = lyt_hr,
      var = "row_type",
      split_fun = keep_split_levels("analysis"),
      nested = FALSE,
      child_labels = "hidden"
    )
    lyt_hr <- split_rows_by(lyt = lyt_hr, var = "var_label", nested = TRUE)
    lyt_hr <- analyze_colvars(
      lyt = lyt_hr,
      afun = afun_lst[names(colvars_hr$labels)],
      na_str = na_str,
      inclNAs = TRUE,
      extra_args = extra_args
    )
  }

  table_hr <- build_table(lyt_hr, df = df$hr)

  # Join tables, add forest plot attributes
  n_tot_ids <- grep("^n_tot", colvars_hr$vars)
  if (is.null(table_survtime)) {
    result <- table_hr
    hr_id <- match("hr", colvars_hr$vars)
    ci_id <- match("ci", colvars_hr$vars)
  } else {
    result <- cbind_rtables(table_hr[, n_tot_ids], table_survtime, table_hr[, -n_tot_ids])
    hr_id <- length(n_tot_ids) + ncol(table_survtime) + match("hr", colvars_hr$vars[-n_tot_ids])
    ci_id <- length(n_tot_ids) + ncol(table_survtime) + match("ci", colvars_hr$vars[-n_tot_ids])
    n_tot_ids <- seq_along(n_tot_ids)
  }
  structure(
    result,
    forest_header = paste0(rev(levels(df$survtime$arm)), "\nBetter"),
    col_x = hr_id,
    col_ci = ci_id,
    col_symbol_size = n_tot_ids[1] # for scaling the symbol sizes in forest plots
  )
}

#' Labels for column variables in survival duration by subgroup table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Internal function to check variables included in [tabulate_survival_subgroups()] and create column labels.
#'
#' @inheritParams tabulate_survival_subgroups
#' @inheritParams argument_convention
#' @param method (`string`)\cr p-value method for testing hazard ratio = 1.
#'
#' @return A `list` of variables and their labels to tabulate.
#'
#' @note At least one of `n_tot` and `n_tot_events` must be provided in `vars`.
#'
#' @export
d_survival_subgroups_colvars <- function(vars,
                                         conf_level,
                                         method,
                                         time_unit = NULL) {
  checkmate::assert_character(vars)
  checkmate::assert_string(time_unit, null.ok = TRUE)
  checkmate::assert_subset(c("hr", "ci"), vars)
  checkmate::assert_true(any(c("n_tot", "n_tot_events") %in% vars))
  checkmate::assert_subset(
    vars,
    c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval")
  )

  propcase_time_label <- if (!is.null(time_unit)) {
    paste0("Median (", time_unit, ")")
  } else {
    "Median"
  }

  varlabels <- c(
    n = "n",
    n_events = "Events",
    median = propcase_time_label,
    n_tot = "Total n",
    n_tot_events = "Total Events",
    hr = "Hazard Ratio",
    ci = paste0(100 * conf_level, "% Wald CI"),
    pval = method
  )

  colvars <- vars

  # The `lcl` variable is just a placeholder available in the analysis data,
  # it is not acutally used in the tabulation.
  # Variables used in the tabulation are lcl and ucl, see `a_survival_subgroups` for details.
  colvars[colvars == "ci"] <- "lcl"

  list(
    vars = colvars,
    labels = varlabels[vars]
  )
}

#' Create a forest plot from an `rtable`
#'
#' Given a [rtables::rtable()] object with at least one column with a single value and one column with 2
#' values, converts table to a [ggplot2::ggplot()] object and generates an accompanying forest plot. The
#' table and forest plot are printed side-by-side.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams rtable2gg
#' @inheritParams argument_convention
#' @param tbl (`VTableTree`)\cr `rtables` table with at least one column with a single value and one column with 2
#'   values.
#' @param col_x (`integer(1)` or `NULL`)\cr column index with estimator. By default tries to get this from
#'   `tbl` attribute `col_x`, otherwise needs to be manually specified. If `NULL`, points will be excluded
#'   from forest plot.
#' @param col_ci (`integer(1)` or `NULL`)\cr column index with confidence intervals. By default tries to get this from
#'   `tbl` attribute `col_ci`, otherwise needs to be manually specified. If `NULL`, lines will be excluded
#'   from forest plot.
#' @param vline (`numeric(1)` or `NULL`)\cr x coordinate for vertical line, if `NULL` then the line is omitted.
#' @param forest_header (`character(2)`)\cr text displayed to the left and right of `vline`, respectively.
#'   If `vline = NULL` then `forest_header` is not printed. By default tries to get this from `tbl` attribute
#'   `forest_header`. If `NULL`, defaults will be extracted from the table if possible, and set to
#'   `"Comparison\nBetter"` and `"Treatment\nBetter"` if not.
#' @param xlim (`numeric(2)`)\cr limits for x axis.
#' @param logx (`flag`)\cr show the x-values on logarithm scale.
#' @param x_at (`numeric`)\cr x-tick locations, if `NULL`, `x_at` is set to `vline` and both `xlim` values.
#' @param width_row_names `r lifecycle::badge("deprecated")` Please use the `lbl_col_padding` argument instead.
#' @param width_columns (`numeric`)\cr a vector of column widths. Each element's position in
#'   `colwidths` corresponds to the column of `tbl` in the same position. If `NULL`, column widths are calculated
#'   according to maximum number of characters per column.
#' @param width_forest `r lifecycle::badge("deprecated")` Please use the `rel_width_forest` argument instead.
#' @param rel_width_forest (`proportion`)\cr proportion of total width to allocate to the forest plot. Relative
#'   width of table is then `1 - rel_width_forest`. If `as_list = TRUE`, this parameter is ignored.
#' @param font_size (`numeric(1)`)\cr font size.
#' @param col_symbol_size (`numeric` or `NULL`)\cr column index from `tbl` containing data to be used
#'   to determine relative size for estimator plot symbol. Typically, the symbol size is proportional
#'   to the sample size used to calculate the estimator. If `NULL`, the same symbol size is used for all subgroups.
#'   By default tries to get this from `tbl` attribute `col_symbol_size`, otherwise needs to be manually specified.
#' @param col (`character`)\cr color(s).
#' @param ggtheme (`theme`)\cr a graphical theme as provided by `ggplot2` to control styling of the plot.
#' @param as_list (`flag`)\cr whether the two `ggplot` objects should be returned as a list. If `TRUE`, a named list
#'   with two elements, `table` and `plot`, will be returned. If `FALSE` (default) the table and forest plot are
#'   printed side-by-side via [cowplot::plot_grid()].
#' @param gp `r lifecycle::badge("deprecated")` `g_forest` is now generated as a `ggplot` object. This argument
#'   is no longer used.
#' @param draw `r lifecycle::badge("deprecated")` `g_forest` is now generated as a `ggplot` object. This argument
#'   is no longer used.
#' @param newpage `r lifecycle::badge("deprecated")` `g_forest` is now generated as a `ggplot` object. This argument
#'   is no longer used.
#'
#' @return `ggplot` forest plot and table.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' n_records <- 20
#' adrs_labels <- formatters::var_labels(adrs, fill = TRUE)
#' adrs <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
#'   slice(seq_len(n_records)) %>%
#'   droplevels() %>%
#'   mutate(
#'     # Reorder levels of factor to make the placebo group the reference arm.
#'     ARM = fct_relevel(ARM, "B: Placebo"),
#'     rsp = AVALC == "CR"
#'   )
#' formatters::var_labels(adrs) <- c(adrs_labels, "Response")
#' df <- extract_rsp_subgroups(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "STRATA2")),
#'   data = adrs
#' )
#' # Full commonly used response table.
#'
#' tbl <- basic_table() %>%
#'   tabulate_rsp_subgroups(df)
#' g_forest(tbl)
#'
#' # Odds ratio only table.
#'
#' tbl_or <- basic_table() %>%
#'   tabulate_rsp_subgroups(df, vars = c("n_tot", "or", "ci"))
#' g_forest(
#'   tbl_or,
#'   forest_header = c("Comparison\nBetter", "Treatment\nBetter")
#' )
#'
#' # Survival forest plot example.
#' adtte <- tern_ex_adtte
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte, fill = TRUE)
#' adtte_f <- adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X"),
#'     SEX %in% c("M", "F")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
#'     SEX = droplevels(SEX),
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- list(
#'   "ARM" = adtte_labels["ARM"],
#'   "SEX" = adtte_labels["SEX"],
#'   "AVALU" = adtte_labels["AVALU"],
#'   "is_event" = "Event Flag"
#' )
#' formatters::var_labels(adtte_f)[names(labels)] <- as.character(labels)
#' df <- extract_survival_subgroups(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM", subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#' table_hr <- basic_table() %>%
#'   tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
#' g_forest(table_hr)
#'
#' # Works with any `rtable`.
#' tbl <- rtable(
#'   header = c("E", "CI", "N"),
#'   rrow("", 1, c(.8, 1.2), 200),
#'   rrow("", 1.2, c(1.1, 1.4), 50)
#' )
#' g_forest(
#'   tbl = tbl,
#'   col_x = 1,
#'   col_ci = 2,
#'   xlim = c(0.5, 2),
#'   x_at = c(0.5, 1, 2),
#'   col_symbol_size = 3
#' )
#'
#' tbl <- rtable(
#'   header = rheader(
#'     rrow("", rcell("A", colspan = 2)),
#'     rrow("", "c1", "c2")
#'   ),
#'   rrow("row 1", 1, c(.8, 1.2)),
#'   rrow("row 2", 1.2, c(1.1, 1.4))
#' )
#' g_forest(
#'   tbl = tbl,
#'   col_x = 1,
#'   col_ci = 2,
#'   xlim = c(0.5, 2),
#'   x_at = c(0.5, 1, 2),
#'   vline = 1,
#'   forest_header = c("Hello", "World")
#' )
#'
#' @export
g_forest <- function(tbl,
                     col_x = attr(tbl, "col_x"),
                     col_ci = attr(tbl, "col_ci"),
                     vline = 1,
                     forest_header = attr(tbl, "forest_header"),
                     xlim = c(0.1, 10),
                     logx = TRUE,
                     x_at = c(0.1, 1, 10),
                     width_row_names = lifecycle::deprecated(),
                     width_columns = NULL,
                     width_forest = lifecycle::deprecated(),
                     lbl_col_padding = 0,
                     rel_width_forest = 0.25,
                     font_size = 12,
                     col_symbol_size = attr(tbl, "col_symbol_size"),
                     col = getOption("ggplot2.discrete.colour")[1],
                     ggtheme = NULL,
                     as_list = FALSE,
                     gp = lifecycle::deprecated(),
                     draw = lifecycle::deprecated(),
                     newpage = lifecycle::deprecated()) {
  # Deprecated argument warnings
  if (lifecycle::is_present(width_row_names)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_forest(width_row_names)", "g_forest(lbl_col_padding)",
      details = "The width of the row label column can be adjusted via the `lbl_col_padding` parameter."
    )
  }
  if (lifecycle::is_present(width_forest)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_forest(width_forest)", "g_forest(rel_width_forest)",
      details = "Relative width of the forest plot (as a proportion) can be set via the `rel_width_forest` parameter."
    )
  }
  if (lifecycle::is_present(gp)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_forest(gp)", "g_forest(ggtheme)",
      details = paste(
        "`g_forest` is now generated as a `ggplot` object.",
        "Additional display settings should be supplied via the `ggtheme` parameter."
      )
    )
  }
  if (lifecycle::is_present(draw)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_forest(draw)",
      details = "`g_forest` now generates `ggplot` objects. This parameter has no effect."
    )
  }
  if (lifecycle::is_present(newpage)) {
    lifecycle::deprecate_warn(
      "0.9.4", "g_forest(newpage)",
      details = "`g_forest` now generates `ggplot` objects. This parameter has no effect."
    )
  }

  checkmate::assert_class(tbl, "VTableTree")
  checkmate::assert_number(col_x, lower = 0, upper = ncol(tbl), null.ok = TRUE)
  checkmate::assert_number(col_ci, lower = 0, upper = ncol(tbl), null.ok = TRUE)
  checkmate::assert_number(col_symbol_size, lower = 0, upper = ncol(tbl), null.ok = TRUE)
  checkmate::assert_number(font_size, lower = 0)
  checkmate::assert_character(col, null.ok = TRUE)
  checkmate::assert_true(is.null(col) | length(col) == 1 | length(col) == nrow(tbl))

  # Extract info from table
  mat <- matrix_form(tbl, indent_rownames = TRUE)
  mat_strings <- formatters::mf_strings(mat)
  nlines_hdr <- formatters::mf_nlheader(mat)
  nrows_body <- nrow(mat_strings) - nlines_hdr
  tbl_stats <- mat_strings[nlines_hdr, -1]

  # Generate and modify table as ggplot object
  gg_table <- rtable2gg(tbl, fontsize = font_size, colwidths = width_columns, lbl_col_padding = lbl_col_padding) +
    theme(plot.margin = margin(0, 0, 0, 0.025, "npc"))
  gg_table$scales$scales[[1]]$expand <- c(0.01, 0.01)
  gg_table$scales$scales[[2]]$limits[2] <- nrow(mat_strings) + 1
  if (nlines_hdr == 2) {
    gg_table$scales$scales[[2]]$expand <- c(0, 0)
    arms <- unique(mat_strings[1, ][nzchar(trimws(mat_strings[1, ]))])
  } else {
    arms <- NULL
  }

  tbl_df <- as_result_df(tbl)
  dat_cols <- seq(which(names(tbl_df) == "node_class") + 1, ncol(tbl_df))
  tbl_df <- tbl_df[, c(which(names(tbl_df) == "row_num"), dat_cols)]
  names(tbl_df) <- c("row_num", tbl_stats)

  # Check table data columns
  if (!is.null(col_ci)) {
    ci_col <- col_ci + 1
  } else {
    tbl_df[["empty_ci"]] <- rep(list(c(NA_real_, NA_real_)), nrow(tbl_df))
    ci_col <- which(names(tbl_df) == "empty_ci")
  }
  if (length(tbl_df[, ci_col][[1]]) != 2) stop("CI column must have two elements (lower and upper limits).")

  if (!is.null(col_x)) {
    x_col <- col_x + 1
  } else {
    tbl_df[["empty_x"]] <- NA_real_
    x_col <- which(names(tbl_df) == "empty_x")
  }
  if (!is.null(col_symbol_size)) {
    sym_size <- unlist(tbl_df[, col_symbol_size + 1])
  } else {
    sym_size <- rep(1, nrow(tbl_df))
  }

  tbl_df[, c("ci_lwr", "ci_upr")] <- t(sapply(tbl_df[, ci_col], unlist))
  x <- unlist(tbl_df[, x_col])
  lwr <- unlist(tbl_df[["ci_lwr"]])
  upr <- unlist(tbl_df[["ci_upr"]])
  row_num <- nrow(mat_strings) - tbl_df[["row_num"]] - as.numeric(nlines_hdr == 2)

  if (is.null(col)) col <- "#343cff"
  if (length(col) == 1) col <- rep(col, nrow(tbl_df))
  if (is.null(x_at)) x_at <- union(xlim, vline)
  x_labels <- x_at

  # Apply log transformation
  if (logx) {
    x_t <- log(x)
    lwr_t <- log(lwr)
    upr_t <- log(upr)
    xlim_t <- log(xlim)
  } else {
    x_t <- x
    lwr_t <- lwr
    upr_t <- upr
    xlim_t <- xlim
  }

  # Set up plot area
  gg_plt <- ggplot(data = tbl_df) +
    theme(
      panel.background = element_rect(fill = "transparent", color = NA_character_),
      plot.background = element_rect(fill = "transparent", color = NA_character_),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.line.x = element_line(),
      axis.text = element_text(size = font_size),
      legend.position = "none",
      plot.margin = margin(0, 0.1, 0.05, 0, "npc")
    ) +
    scale_x_continuous(
      transform = ifelse(logx, "log", "identity"),
      limits = xlim,
      breaks = x_at,
      labels = x_labels,
      expand = c(0.01, 0)
    ) +
    scale_y_continuous(
      limits = c(0, nrow(mat_strings) + 1),
      breaks = NULL,
      expand = c(0, 0)
    ) +
    coord_cartesian(clip = "off")

  if (is.null(ggtheme)) {
    gg_plt <- gg_plt + annotate(
      "rect",
      xmin = xlim[1],
      xmax = xlim[2],
      ymin = 0,
      ymax = nrows_body + 0.5,
      fill = "grey92"
    )
  }

  if (!is.null(vline)) {
    # Set default forest header
    if (is.null(forest_header)) {
      forest_header <- c(
        paste(if (length(arms) == 2) arms[1] else "Comparison", "Better", sep = "\n"),
        paste(if (length(arms) == 2) arms[2] else "Treatment", "Better", sep = "\n")
      )
    }

    # Add vline and forest header labels
    mid_pts <- if (logx) {
      c(exp(mean(log(c(xlim[1], vline)))), exp(mean(log(c(vline, xlim[2])))))
    } else {
      c(mean(c(xlim[1], vline)), mean(c(vline, xlim[2])))
    }
    gg_plt <- gg_plt +
      annotate(
        "segment",
        x = vline, xend = vline, y = 0, yend = nrows_body + 0.5
      ) +
      annotate(
        "text",
        x = mid_pts[1], y = nrows_body + 1.25,
        label = forest_header[1],
        size = font_size / .pt,
        lineheight = 0.9
      ) +
      annotate(
        "text",
        x = mid_pts[2], y = nrows_body + 1.25,
        label = forest_header[2],
        size = font_size / .pt,
        lineheight = 0.9
      )
  }

  # Add points to plot
  if (any(!is.na(x_t))) {
    x_t[x < xlim[1] | x > xlim[2]] <- NA
    gg_plt <- gg_plt + geom_point(
      x = x_t,
      y = row_num,
      color = col,
      aes(size = sym_size),
      na.rm = TRUE
    )
  }

  for (i in seq_len(nrow(tbl_df))) {
    # Determine which arrow(s) to add to CI lines
    which_arrow <- c(lwr_t[i] < xlim_t[1], upr_t[i] > xlim_t[2])
    which_arrow <- dplyr::case_when(
      all(which_arrow) ~ "both",
      which_arrow[1] ~ "first",
      which_arrow[2] ~ "last",
      TRUE ~ NA_character_
    )

    # Add CI lines
    gg_plt <- gg_plt +
      if (!is.na(which_arrow)) {
        annotate(
          "segment",
          x = if (!which_arrow %in% c("first", "both")) lwr[i] else xlim[1],
          xend = if (!which_arrow %in% c("last", "both")) upr[i] else xlim[2],
          y = row_num[i], yend = row_num[i],
          color = if (length(col) == 1) col else col[i],
          arrow = arrow(length = unit(0.05, "npc"), ends = which_arrow),
          na.rm = TRUE
        )
      } else {
        annotate(
          "segment",
          x = lwr[i], xend = upr[i],
          y = row_num[i], yend = row_num[i],
          color = if (length(col) == 1) col else col[i],
          na.rm = TRUE
        )
      }
  }

  # Apply custom ggtheme to plot
  if (!is.null(ggtheme)) gg_plt <- gg_plt + ggtheme

  if (as_list) {
    list(
      table = gg_table,
      plot = gg_plt
    )
  } else {
    cowplot::plot_grid(
      gg_table,
      gg_plt,
      align = "h",
      axis = "tblr",
      rel_widths = c(1 - rel_width_forest, rel_width_forest)
    )
  }
}

#' Forest plot grob
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' @inheritParams g_forest
#' @param tbl (`VTableTree`)\cr `rtables` table object.
#' @param x (`numeric`)\cr coordinate of point.
#' @param lower,upper (`numeric`)\cr lower/upper bound of the confidence interval.
#' @param symbol_size (`numeric`)\cr vector with relative size for plot symbol.
#'   If `NULL`, the same symbol size is used.
#'
#' @details
#' The heights get automatically determined.
#'
#' @examples
#' tbl <- rtable(
#'   header = rheader(
#'     rrow("", "E", rcell("CI", colspan = 2), "N"),
#'     rrow("", "A", "B", "C", "D")
#'   ),
#'   rrow("row 1", 1, 0.8, 1.1, 16),
#'   rrow("row 2", 1.4, 0.8, 1.6, 25),
#'   rrow("row 3", 1.2, 0.8, 1.6, 36)
#' )
#'
#' x <- c(1, 1.4, 1.2)
#' lower <- c(0.8, 0.8, 0.8)
#' upper <- c(1.1, 1.6, 1.6)
#' # numeric vector with multiplication factor to scale each circle radius
#' # default radius is 1/3.5 lines
#' symbol_scale <- c(1, 1.25, 1.5)
#'
#' # Internal function - forest_grob
#' \donttest{
#' p <- forest_grob(tbl, x, lower, upper,
#'   vline = 1, forest_header = c("A", "B"),
#'   x_at = c(.1, 1, 10), xlim = c(0.1, 10), logx = TRUE, symbol_size = symbol_scale,
#'   vp = grid::plotViewport(margins = c(1, 1, 1, 1))
#' )
#'
#' draw_grob(p)
#' }
#'
#' @noRd
#' @keywords internal
forest_grob <- function(tbl,
                        x,
                        lower,
                        upper,
                        vline,
                        forest_header,
                        xlim = NULL,
                        logx = FALSE,
                        x_at = NULL,
                        width_row_names = NULL,
                        width_columns = NULL,
                        width_forest = grid::unit(1, "null"),
                        symbol_size = NULL,
                        col = "blue",
                        name = NULL,
                        gp = NULL,
                        vp = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4", "forest_grob()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  nr <- nrow(tbl)
  if (is.null(vline)) {
    checkmate::assert_true(is.null(forest_header))
  } else {
    checkmate::assert_number(vline)
    checkmate::assert_character(forest_header, len = 2, null.ok = TRUE)
  }

  checkmate::assert_numeric(x, len = nr)
  checkmate::assert_numeric(lower, len = nr)
  checkmate::assert_numeric(upper, len = nr)
  checkmate::assert_numeric(symbol_size, len = nr, null.ok = TRUE)
  checkmate::assert_character(col)

  if (is.null(symbol_size)) {
    symbol_size <- rep(1, nr)
  }

  if (is.null(xlim)) {
    r <- range(c(x, lower, upper), na.rm = TRUE)
    xlim <- r + c(-0.05, 0.05) * diff(r)
  }

  if (logx) {
    if (is.null(x_at)) {
      x_at <- pretty(log(stats::na.omit(c(x, lower, upper))))
      x_labels <- exp(x_at)
    } else {
      x_labels <- x_at
      x_at <- log(x_at)
    }
    xlim <- log(xlim)
    x <- log(x)
    lower <- log(lower)
    upper <- log(upper)
    if (!is.null(vline)) {
      vline <- log(vline)
    }
  } else {
    x_labels <- TRUE
  }

  data_forest_vp <- grid::dataViewport(xlim, c(0, 1))

  # Get table content as matrix form.
  mf <- matrix_form(tbl)

  # Use `rtables` indent_string eventually.
  mf$strings[, 1] <- paste0(
    strrep("    ", c(rep(0, attr(mf, "nrow_header")), mf$row_info$indent)),
    mf$strings[, 1]
  )

  n_header <- attr(mf, "nrow_header")

  if (any(mf$display[, 1] == FALSE)) stop("row names need to be always displayed")

  # Pre-process the data to be used in lapply and cell_in_rows.
  to_args_for_cell_in_rows_fun <- function(part = c("body", "header"),
                                           underline_colspan = FALSE) {
    part <- match.arg(part)
    if (part == "body") {
      mat_row_indices <- seq_len(nrow(tbl)) + n_header
      row_ind_offset <- -n_header
    } else {
      mat_row_indices <- seq_len(n_header)
      row_ind_offset <- 0
    }

    lapply(mat_row_indices, function(i) {
      disp <- mf$display[i, -1]
      list(
        row_name = mf$strings[i, 1],
        cells = mf$strings[i, -1][disp],
        cell_spans = mf$spans[i, -1][disp],
        row_index = i + row_ind_offset,
        underline_colspan = underline_colspan
      )
    })
  }

  args_header <- to_args_for_cell_in_rows_fun("header", underline_colspan = TRUE)
  args_body <- to_args_for_cell_in_rows_fun("body", underline_colspan = FALSE)

  grid::gTree(
    name = name,
    children = grid::gList(
      grid::gTree(
        children = do.call(grid::gList, lapply(args_header, do.call, what = cell_in_rows)),
        vp = grid::vpPath("vp_table_layout", "vp_header")
      ),
      grid::gTree(
        children = do.call(grid::gList, lapply(args_body, do.call, what = cell_in_rows)),
        vp = grid::vpPath("vp_table_layout", "vp_body")
      ),
      grid::linesGrob(
        grid::unit(c(0, 1), "npc"),
        y = grid::unit(c(.5, .5), "npc"),
        vp = grid::vpPath("vp_table_layout", "vp_spacer")
      ),
      # forest part
      if (is.null(vline)) {
        NULL
      } else {
        grid::gTree(
          children = grid::gList(
            grid::gTree(
              children = grid::gList(
                # this may overflow, to fix, look here
                # https://stackoverflow.com/questions/33623169/add-multi-line-footnote-to-tablegrob-while-using-gridextra-in-r # nolint
                grid::textGrob(
                  forest_header[1],
                  x = grid::unit(vline, "native") - grid::unit(1, "lines"),
                  just = c("right", "center")
                ),
                grid::textGrob(
                  forest_header[2],
                  x = grid::unit(vline, "native") + grid::unit(1, "lines"),
                  just = c("left", "center")
                )
              ),
              vp = grid::vpStack(grid::viewport(layout.pos.col = ncol(tbl) + 2), data_forest_vp)
            )
          ),
          vp = grid::vpPath("vp_table_layout", "vp_header")
        )
      },
      grid::gTree(
        children = grid::gList(
          grid::gTree(
            children = grid::gList(
              grid::rectGrob(gp = grid::gpar(col = "gray90", fill = "gray90")),
              if (is.null(vline)) {
                NULL
              } else {
                grid::linesGrob(
                  x = grid::unit(rep(vline, 2), "native"),
                  y = grid::unit(c(0, 1), "npc"),
                  gp = grid::gpar(lwd = 2),
                  vp = data_forest_vp
                )
              },
              grid::xaxisGrob(at = x_at, label = x_labels, vp = data_forest_vp)
            ),
            vp = grid::viewport(layout.pos.col = ncol(tbl) + 2)
          )
        ),
        vp = grid::vpPath("vp_table_layout", "vp_body")
      ),
      grid::gTree(
        children = do.call(
          grid::gList,
          Map(
            function(xi, li, ui, row_index, size_i, col) {
              forest_dot_line(
                xi,
                li,
                ui,
                row_index,
                xlim,
                symbol_size = size_i,
                col = col,
                datavp = data_forest_vp
              )
            },
            x,
            lower,
            upper,
            seq_along(x),
            symbol_size,
            col,
            USE.NAMES = FALSE
          )
        ),
        vp = grid::vpPath("vp_table_layout", "vp_body")
      )
    ),
    childrenvp = forest_viewport(tbl, width_row_names, width_columns, width_forest),
    vp = vp,
    gp = gp
  )
}

cell_in_rows <- function(row_name,
                         cells,
                         cell_spans,
                         row_index,
                         underline_colspan = FALSE) {
  checkmate::assert_string(row_name)
  checkmate::assert_character(cells, min.len = 1, any.missing = FALSE)
  checkmate::assert_numeric(cell_spans, len = length(cells), any.missing = FALSE)
  checkmate::assert_number(row_index)
  checkmate::assert_flag(underline_colspan)

  vp_name_rn <- paste0("rowname-", row_index)
  g_rowname <- if (!is.null(row_name) && row_name != "") {
    grid::textGrob(
      name = vp_name_rn,
      label = row_name,
      x = grid::unit(0, "npc"),
      just = c("left", "center"),
      vp = grid::vpPath(paste0("rowname-", row_index))
    )
  } else {
    NULL
  }

  gl_cols <- if (!(length(cells) > 0)) {
    list(NULL)
  } else {
    j <- 1 # column index of cell

    lapply(seq_along(cells), function(k) {
      cell_ascii <- cells[[k]]
      cs <- cell_spans[[k]]

      if (is.na(cell_ascii) || is.null(cell_ascii)) {
        cell_ascii <- "NA"
      }

      cell_name <- paste0("g-cell-", row_index, "-", j)

      cell_grobs <- if (identical(cell_ascii, "")) {
        NULL
      } else {
        if (cs == 1) {
          grid::textGrob(
            label = cell_ascii,
            name = cell_name,
            vp = grid::vpPath(paste0("cell-", row_index, "-", j))
          )
        } else {
          # +1 because of rowname
          vp_joined_cols <- grid::viewport(layout.pos.row = row_index, layout.pos.col = seq(j + 1, j + cs))

          lab <- grid::textGrob(
            label = cell_ascii,
            name = cell_name,
            vp = vp_joined_cols
          )

          if (!underline_colspan || grepl("^[[:space:]]*$", cell_ascii)) {
            lab
          } else {
            grid::gList(
              lab,
              grid::linesGrob(
                x = grid::unit.c(grid::unit(.2, "lines"), grid::unit(1, "npc") - grid::unit(.2, "lines")),
                y = grid::unit(c(0, 0), "npc"),
                vp = vp_joined_cols
              )
            )
          }
        }
      }
      j <<- j + cs

      cell_grobs
    })
  }

  grid::gList(
    g_rowname,
    do.call(grid::gList, gl_cols)
  )
}

#' Graphic object: forest dot line
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Calculate the `grob` corresponding to the dot line within the forest plot.
#'
#' @noRd
#' @keywords internal
forest_dot_line <- function(x,
                            lower,
                            upper,
                            row_index,
                            xlim,
                            symbol_size = 1,
                            col = "blue",
                            datavp) {
  lifecycle::deprecate_warn(
    "0.9.4", "forest_dot_line()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  ci <- c(lower, upper)
  if (any(!is.na(c(x, ci)))) {
    # line
    y <- grid::unit(c(0.5, 0.5), "npc")

    g_line <- if (all(!is.na(ci)) && ci[2] > xlim[1] && ci[1] < xlim[2]) {
      # -
      if (ci[1] >= xlim[1] && ci[2] <= xlim[2]) {
        grid::linesGrob(x = grid::unit(c(ci[1], ci[2]), "native"), y = y)
      } else if (ci[1] < xlim[1] && ci[2] > xlim[2]) {
        # <->
        grid::linesGrob(
          x = grid::unit(xlim, "native"),
          y = y,
          arrow = grid::arrow(angle = 30, length = grid::unit(0.5, "lines"), ends = "both")
        )
      } else if (ci[1] < xlim[1] && ci[2] <= xlim[2]) {
        # <-
        grid::linesGrob(
          x = grid::unit(c(xlim[1], ci[2]), "native"),
          y = y,
          arrow = grid::arrow(angle = 30, length = grid::unit(0.5, "lines"), ends = "first")
        )
      } else if (ci[1] >= xlim[1] && ci[2] > xlim[2]) {
        # ->
        grid::linesGrob(
          x = grid::unit(c(ci[1], xlim[2]), "native"),
          y = y,
          arrow = grid::arrow(angle = 30, length = grid::unit(0.5, "lines"), ends = "last")
        )
      }
    } else {
      NULL
    }

    g_circle <- if (!is.na(x) && x >= xlim[1] && x <= xlim[2]) {
      grid::circleGrob(
        x = grid::unit(x, "native"),
        y = y,
        r = grid::unit(1 / 3.5 * symbol_size, "lines"),
        name = "point"
      )
    } else {
      NULL
    }

    grid::gTree(
      children = grid::gList(
        grid::gTree(
          children = grid::gList(
            grid::gList(
              g_line,
              g_circle
            )
          ),
          vp = datavp,
          gp = grid::gpar(col = col, fill = col)
        )
      ),
      vp = grid::vpPath(paste0("forest-", row_index))
    )
  } else {
    NULL
  }
}

#' Create a viewport tree for the forest plot
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' @param tbl (`VTableTree`)\cr `rtables` table object.
#' @param width_row_names (`grid::unit`)\cr width of row names.
#' @param width_columns (`grid::unit`)\cr width of column spans.
#' @param width_forest (`grid::unit`)\cr width of the forest plot.
#' @param gap_column (`grid::unit`)\cr gap width between the columns.
#' @param gap_header (`grid::unit`)\cr gap width between the header.
#' @param mat_form (`MatrixPrintForm`)\cr matrix print form of the table.
#'
#' @return A viewport tree.
#'
#' @examples
#' library(grid)
#'
#' tbl <- rtable(
#'   header = rheader(
#'     rrow("", "E", rcell("CI", colspan = 2)),
#'     rrow("", "A", "B", "C")
#'   ),
#'   rrow("row 1", 1, 0.8, 1.1),
#'   rrow("row 2", 1.4, 0.8, 1.6),
#'   rrow("row 3", 1.2, 0.8, 1.2)
#' )
#'
#' \donttest{
#' v <- forest_viewport(tbl)
#'
#' grid::grid.newpage()
#' showViewport(v)
#' }
#'
#' @export
forest_viewport <- function(tbl,
                            width_row_names = NULL,
                            width_columns = NULL,
                            width_forest = grid::unit(1, "null"),
                            gap_column = grid::unit(1, "lines"),
                            gap_header = grid::unit(1, "lines"),
                            mat_form = NULL) {
  lifecycle::deprecate_warn(
    "0.9.4",
    "forest_viewport()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  checkmate::assert_class(tbl, "VTableTree")
  checkmate::assert_true(grid::is.unit(width_forest))
  if (!is.null(width_row_names)) {
    checkmate::assert_true(grid::is.unit(width_row_names))
  }
  if (!is.null(width_columns)) {
    checkmate::assert_true(grid::is.unit(width_columns))
  }

  if (is.null(mat_form)) mat_form <- matrix_form(tbl)

  mat_form$strings[!mat_form$display] <- ""

  nr <- nrow(tbl)
  nc <- ncol(tbl)
  nr_h <- attr(mat_form, "nrow_header")

  if (is.null(width_row_names) || is.null(width_columns)) {
    tbl_widths <- formatters::propose_column_widths(mat_form)
    strs_with_width <- strrep("x", tbl_widths) # that works for mono spaced fonts
    if (is.null(width_row_names)) width_row_names <- grid::stringWidth(strs_with_width[1])
    if (is.null(width_columns)) width_columns <- grid::stringWidth(strs_with_width[-1])
  }

  # Widths for row name, cols, forest.
  widths <- grid::unit.c(
    width_row_names + gap_column,
    width_columns + gap_column,
    width_forest
  )

  n_lines_per_row <- apply(
    X = mat_form$strings,
    MARGIN = 1,
    FUN = function(row) {
      tmp <- vapply(
        gregexpr("\n", row, fixed = TRUE),
        attr, numeric(1),
        "match.length"
      ) + 1
      max(c(tmp, 1))
    }
  )

  i_header <- seq_len(nr_h)

  height_body_rows <- grid::unit(n_lines_per_row[-i_header] * 1.2, "lines")
  height_header_rows <- grid::unit(n_lines_per_row[i_header] * 1.2, "lines")

  height_body <- grid::unit(sum(n_lines_per_row[-i_header]) * 1.2, "lines")
  height_header <- grid::unit(sum(n_lines_per_row[i_header]) * 1.2, "lines")

  nc_g <- nc + 2 # number of columns incl. row names and forest

  vp_tbl <- grid::vpTree(
    parent = grid::viewport(
      name = "vp_table_layout",
      layout = grid::grid.layout(
        nrow = 3, ncol = 1,
        heights = grid::unit.c(height_header, gap_header, height_body)
      )
    ),
    children = grid::vpList(
      vp_forest_table_part(nr_h, nc_g, 1, 1, widths, height_header_rows, "vp_header"),
      vp_forest_table_part(nr, nc_g, 3, 1, widths, height_body_rows, "vp_body"),
      grid::viewport(name = "vp_spacer", layout.pos.row = 2, layout.pos.col = 1)
    )
  )
  vp_tbl
}

#' Viewport forest plot: table part
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Prepares a viewport for the table included in the forest plot.
#'
#' @noRd
#' @keywords internal
vp_forest_table_part <- function(nrow,
                                 ncol,
                                 l_row,
                                 l_col,
                                 widths,
                                 heights,
                                 name) {
  lifecycle::deprecate_warn(
    "0.9.4", "vp_forest_table_part()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  grid::vpTree(
    grid::viewport(
      name = name,
      layout.pos.row = l_row,
      layout.pos.col = l_col,
      layout = grid::grid.layout(nrow = nrow, ncol = ncol, widths = widths, heights = heights)
    ),
    children = grid::vpList(
      do.call(
        grid::vpList,
        lapply(
          seq_len(nrow), function(i) {
            grid::viewport(layout.pos.row = i, layout.pos.col = 1, name = paste0("rowname-", i))
          }
        )
      ),
      do.call(
        grid::vpList,
        apply(
          expand.grid(seq_len(nrow), seq_len(ncol - 2)),
          1,
          function(x) {
            i <- x[1]
            j <- x[2]
            grid::viewport(layout.pos.row = i, layout.pos.col = j + 1, name = paste0("cell-", i, "-", j))
          }
        )
      ),
      do.call(
        grid::vpList,
        lapply(
          seq_len(nrow),
          function(i) {
            grid::viewport(layout.pos.row = i, layout.pos.col = ncol, name = paste0("forest-", i))
          }
        )
      )
    )
  )
}

#' Forest rendering
#'
#' @description `r lifecycle::badge("deprecated")`
#'
#' Renders the forest grob.
#'
#' @noRd
#' @keywords internal
grid.forest <- function(...) { # nolint
  lifecycle::deprecate_warn(
    "0.9.4", "grid.forest()",
    details = "`g_forest` now generates `ggplot` objects. This function is no longer used within `tern`."
  )

  grid::grid.draw(forest_grob(...))
}

#' Split function to configure risk difference column
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Wrapper function for [rtables::add_combo_levels()] which configures settings for the risk difference
#' column to be added to an `rtables` object. To add a risk difference column to a table, this function
#' should be used as `split_fun` in calls to [rtables::split_cols_by()], followed by setting argument
#' `riskdiff` to `TRUE` in all following analyze function calls.
#'
#' @param arm_x (`string`)\cr name of reference arm to use in risk difference calculations.
#' @param arm_y (`character`)\cr names of one or more arms to compare to reference arm in risk difference
#'   calculations. A new column will be added for each value of `arm_y`.
#' @param col_label (`character`)\cr labels to use when rendering the risk difference column within the table.
#'   If more than one comparison arm is specified in `arm_y`, default labels will specify which two arms are
#'   being compared (reference arm vs. comparison arm).
#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
#'
#' @return A closure suitable for use as a split function (`split_fun`) within [rtables::split_cols_by()]
#'   when creating a table layout.
#'
#' @seealso [stat_propdiff_ci()] for details on risk difference calculation.
#'
#' @examples
#' adae <- tern_ex_adae
#' adae$AESEV <- factor(adae$AESEV)
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", split_fun = add_riskdiff(arm_x = "ARM A", arm_y = c("ARM B", "ARM C"))) %>%
#'   count_occurrences_by_grade(
#'     var = "AESEV",
#'     riskdiff = TRUE
#'   )
#'
#' tbl <- build_table(lyt, df = adae)
#' tbl
#'
#' @export
add_riskdiff <- function(arm_x,
                         arm_y,
                         col_label = paste0(
                           "Risk Difference (%) (95% CI)", if (length(arm_y) > 1) paste0("\n", arm_x, " vs. ", arm_y)
                         ),
                         pct = TRUE) {
  checkmate::assert_character(arm_x, len = 1)
  checkmate::assert_character(arm_y, min.len = 1)
  checkmate::assert_character(col_label, len = length(arm_y))

  combodf <- tibble::tribble(~valname, ~label, ~levelcombo, ~exargs)
  for (i in seq_len(length(arm_y))) {
    combodf <- rbind(
      combodf,
      tibble::tribble(
        ~valname, ~label, ~levelcombo, ~exargs,
        paste("riskdiff", arm_x, arm_y[i], sep = "_"), col_label[i], c(arm_x, arm_y[i]), list()
      )
    )
  }
  if (pct) combodf$valname <- paste0(combodf$valname, "_pct")
  add_combo_levels(combodf)
}

#' Analysis function to calculate risk difference column values
#'
#' In the risk difference column, this function uses the statistics function associated with `afun` to
#' calculates risk difference values from arm X (reference group) and arm Y. These arms are specified
#' when configuring the risk difference column which is done using the [add_riskdiff()] split function in
#' the previous call to [rtables::split_cols_by()]. For all other columns, applies `afun` as usual. This
#' function utilizes the [stat_propdiff_ci()] function to perform risk difference calculations.
#'
#' @inheritParams argument_convention
#' @param afun (named `list`)\cr a named list containing one name-value pair where the name corresponds to
#'   the name of the statistics function that should be used in calculations and the value is the corresponding
#'   analysis function.
#' @param s_args (named `list`)\cr additional arguments to be passed to the statistics function and analysis
#'   function supplied in `afun`.
#'
#' @return A list of formatted [rtables::CellValue()].
#'
#' @seealso
#' * [stat_propdiff_ci()] for details on risk difference calculation.
#' * Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()] with
#'   `riskdiff` argument set to `TRUE` in subsequent analyze functions calls, adds a risk difference column
#'   to a table layout.
#'
#' @keywords internal
afun_riskdiff <- function(df,
                          labelstr = "",
                          .var,
                          .N_col, # nolint
                          .N_row, # nolint
                          .df_row,
                          .spl_context,
                          .all_col_counts,
                          .stats,
                          .formats = NULL,
                          .labels = NULL,
                          .indent_mods = NULL,
                          na_str = default_na_str(),
                          afun,
                          s_args = list()) {
  if (!any(grepl("riskdiff", names(.spl_context)))) {
    stop(
      "Please set up levels to use in risk difference calculations using the `add_riskdiff` ",
      "split function within `split_cols_by`. See ?add_riskdiff for details."
    )
  }
  checkmate::assert_list(afun, len = 1, types = "function")
  checkmate::assert_named(afun)
  afun_args <- list(
    .var = .var, .df_row = .df_row, .N_row = .N_row, denom = "N_col", labelstr = labelstr,
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  afun_args <- afun_args[intersect(names(afun_args), names(as.list(args(afun[[1]]))))]
  if ("denom" %in% names(s_args)) afun_args[["denom"]] <- NULL

  cur_split <- tail(.spl_context$cur_col_split_val[[1]], 1)
  if (!grepl("^riskdiff", cur_split)) {
    # Apply basic afun (no risk difference) in all other columns
    do.call(afun[[1]], args = c(list(df = df, .N_col = .N_col), afun_args, s_args))
  } else {
    arm_x <- strsplit(cur_split, "_")[[1]][2]
    arm_y <- strsplit(cur_split, "_")[[1]][3]
    if (length(.spl_context$cur_col_split[[1]]) > 1) { # Different split name for nested column splits
      arm_spl_x <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 2)], collapse = ""))
      arm_spl_y <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 3)], collapse = ""))
    } else {
      arm_spl_x <- arm_x
      arm_spl_y <- arm_y
    }

    N_col_x <- .all_col_counts[[arm_spl_x]] # nolint
    N_col_y <- .all_col_counts[[arm_spl_y]] # nolint
    cur_var <- tail(.spl_context$cur_col_split[[1]], 1)

    # Apply statistics function to arm X and arm Y data
    s_args <- c(s_args, afun_args[intersect(names(afun_args), names(as.list(args(names(afun)))))])
    s_x <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_x, ], .N_col = N_col_x), s_args))
    s_y <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_y, ], .N_col = N_col_y), s_args))

    # Get statistic name and row names
    stat <- ifelse("count_fraction" %in% names(s_x), "count_fraction", "unique")
    if ("flag_variables" %in% names(s_args)) {
      var_nms <- s_args$flag_variables
    } else if (is.list(s_x[[stat]]) && !is.null(names(s_x[[stat]]))) {
      var_nms <- names(s_x[[stat]])
    } else {
      var_nms <- ""
      s_x[[stat]] <- list(s_x[[stat]])
      s_y[[stat]] <- list(s_y[[stat]])
    }

    # Calculate risk difference for each row, repeated if multiple statistics in table
    pct <- tail(strsplit(cur_split, "_")[[1]], 1) == "pct"
    rd_ci <- rep(stat_propdiff_ci(
      lapply(s_x[[stat]], `[`, 1), lapply(s_y[[stat]], `[`, 1),
      N_col_x, N_col_y,
      list_names = var_nms,
      pct = pct
    ), max(1, length(.stats)))

    in_rows(.list = rd_ci, .formats = "xx.x (xx.x - xx.x)", .indent_mods = .indent_mods)
  }
}

#' Control function for risk difference column
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Sets a list of parameters to use when generating a risk (proportion) difference column. Used as input to the
#' `riskdiff` parameter of [tabulate_rsp_subgroups()] and [tabulate_survival_subgroups()].
#'
#' @inheritParams add_riskdiff
#' @param format (`string` or `function`)\cr the format label (string) or formatting function to apply to the risk
#'   difference statistic. See the `3d` string options in [formatters::list_valid_format_labels()] for possible format
#'   strings. Defaults to `"xx.x (xx.x - xx.x)"`.
#'
#' @return A `list` of items with names corresponding to the arguments.
#'
#' @seealso [add_riskdiff()], [tabulate_rsp_subgroups()], and [tabulate_survival_subgroups()].
#'
#' @examples
#' control_riskdiff()
#' control_riskdiff(arm_x = "ARM A", arm_y = "ARM B")
#'
#' @export
control_riskdiff <- function(arm_x = NULL,
                             arm_y = NULL,
                             format = "xx.x (xx.x - xx.x)",
                             col_label = "Risk Difference (%) (95% CI)",
                             pct = TRUE) {
  checkmate::assert_character(arm_x, len = 1, null.ok = TRUE)
  checkmate::assert_character(arm_y, min.len = 1, null.ok = TRUE)
  checkmate::assert_character(format, len = 1)
  checkmate::assert_character(col_label)
  checkmate::assert_flag(pct)

  list(arm_x = arm_x, arm_y = arm_y, format = format, col_label = col_label, pct = pct)
}

#' Tabulate biomarker effects on binary response by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The [tabulate_rsp_biomarkers()] function creates a layout element to tabulate the estimated biomarker effects on a
#' binary response endpoint across subgroups, returning statistics including response rate and odds ratio for each
#' population subgroup. The table is created from `df`, a list of data frames returned by [extract_rsp_biomarkers()],
#' with the statistics to include specified via the `vars` parameter.
#'
#' A forest plot can be created from the resulting table using the [g_forest()] function.
#'
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr containing all analysis variables, as returned by
#'   [extract_rsp_biomarkers()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n_tot`: Total number of patients per group.
#'   * `n_rsp`: Total number of responses per group.
#'   * `prop`: Total response proportion per group.
#'   * `or`: Odds ratio.
#'   * `ci`: Confidence interval of odds ratio.
#'   * `pval`: p-value of the effect.
#'   Note, the statistics `n_tot`, `or` and `ci` are required.
#'
#' @return An `rtables` table summarizing biomarker effects on binary response by subgroup.
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. The tables are then typically used as input for forest plots.
#'
#' @note In contrast to [tabulate_rsp_subgroups()] this tabulation function does
#'   not start from an input layout `lyt`. This is because internally the table is
#'   created by combining multiple subtables.
#'
#' @seealso [h_tab_rsp_one_biomarker()] which is used internally, [extract_rsp_biomarkers()].
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   mutate(rsp = AVALC == "CR")
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' df <- extract_rsp_biomarkers(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adrs_f
#' )
#'
#' \donttest{
#' ## Table with default columns.
#' tabulate_rsp_biomarkers(df)
#'
#' ## Table with a manually chosen set of columns: leave out "pval", reorder.
#' tab <- tabulate_rsp_biomarkers(
#'   df = df,
#'   vars = c("n_rsp", "ci", "n_tot", "prop", "or")
#' )
#'
#' ## Finally produce the forest plot.
#' g_forest(tab, xlim = c(0.7, 1.4))
#' }
#'
#' @export
#' @name response_biomarkers_subgroups
tabulate_rsp_biomarkers <- function(df,
                                    vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
                                    na_str = default_na_str(),
                                    .indent_mods = 0L) {
  checkmate::assert_data_frame(df)
  checkmate::assert_character(df$biomarker)
  checkmate::assert_character(df$biomarker_label)
  checkmate::assert_subset(vars, get_stats("tabulate_rsp_biomarkers"))

  # Create "ci" column from "lcl" and "ucl"
  df$ci <- combine_vectors(df$lcl, df$ucl)

  df_subs <- split(df, f = df$biomarker)
  tabs <- lapply(df_subs, FUN = function(df_sub) {
    tab_sub <- h_tab_rsp_one_biomarker(
      df = df_sub,
      vars = vars,
      na_str = na_str,
      .indent_mods = .indent_mods
    )
    # Insert label row as first row in table.
    label_at_path(tab_sub, path = row_paths(tab_sub)[[1]][1]) <- df_sub$biomarker_label[1]
    tab_sub
  })
  result <- do.call(rbind, tabs)

  n_id <- grep("n_tot", vars)
  or_id <- match("or", vars)
  ci_id <- match("ci", vars)
  structure(
    result,
    forest_header = paste0(c("Lower", "Higher"), "\nBetter"),
    col_x = or_id,
    col_ci = ci_id,
    col_symbol_size = n_id
  )
}

#' Prepare response data estimates for multiple biomarkers in a single data frame
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares estimates for number of responses, patients and overall response rate,
#' as well as odds ratio estimates, confidence intervals and p-values,
#' for multiple biomarkers across population subgroups in a single data frame.
#' `variables` corresponds to the names of variables found in `data`, passed as a
#' named list and requires elements `rsp` and `biomarkers` (vector of continuous
#' biomarker variables) and optionally `covariates`, `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams response_subgroups
#' @param control (named `list`)\cr controls for the response definition and the
#'   confidence level produced by [control_logistic()].
#'
#' @return A `data.frame` with columns `biomarker`, `biomarker_label`, `n_tot`, `n_rsp`,
#'   `prop`, `or`, `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`,
#'   `var_label`, and `row_type`.
#'
#' @note You can also specify a continuous variable in `rsp` and then use the
#'   `response_definition` control to convert that internally to a logical
#'   variable reflecting binary response.
#'
#' @seealso [h_logistic_mult_cont_df()] which is used internally.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   mutate(rsp = AVALC == "CR")
#'
#' # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
#' # in logistic regression models with one covariate `RACE`. The subgroups
#' # are defined by the levels of `BMRKR2`.
#' df <- extract_rsp_biomarkers(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adrs_f
#' )
#' df
#'
#' # Here we group the levels of `BMRKR2` manually, and we add a stratification
#' # variable `STRATA1`. We also here use a continuous variable `EOSDY`
#' # which is then binarized internally (response is defined as this variable
#' # being larger than 750).
#' df_grouped <- extract_rsp_biomarkers(
#'   variables = list(
#'     rsp = "EOSDY",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     subgroups = "BMRKR2",
#'     strata = "STRATA1"
#'   ),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   ),
#'   control = control_logistic(
#'     response_definition = "I(response > 750)"
#'   )
#' )
#' df_grouped
#'
#' @export
extract_rsp_biomarkers <- function(variables,
                                   data,
                                   groups_lists = list(),
                                   control = control_logistic(),
                                   label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_rsp_biomarkers() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  assert_list_of_variables(variables)
  checkmate::assert_string(variables$rsp)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_string(label_all)

  # Start with all patients.
  result_all <- h_logistic_mult_cont_df(
    variables = variables,
    data = data,
    control = control
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"
  if (is.null(variables$subgroups)) {
    # Only return result for all patients.
    result_all
  } else {
    # Add subgroups results.
    l_data <- h_split_by_subgroups(
      data,
      variables$subgroups,
      groups_lists = groups_lists
    )
    l_result <- lapply(l_data, function(grp) {
      result <- h_logistic_mult_cont_df(
        variables = variables,
        data = grp$df,
        control = control
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"
    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' Control function for Cox regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Sets a list of parameters for Cox regression fit. Used internally.
#'
#' @inheritParams argument_convention
#' @param pval_method (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
#' @param interaction (`flag`)\cr if `TRUE`, the model includes the interaction between the studied
#'   treatment and candidate covariate. Note that for univariate models without treatment arm, and
#'   multivariate models, no interaction can be used so that this needs to be `FALSE`.
#' @param ties (`string`)\cr among `exact` (equivalent to `DISCRETE` in SAS), `efron` and `breslow`,
#'   see [survival::coxph()]. Note: there is no equivalent of SAS `EXACT` method in R.
#'
#' @return A `list` of items with names corresponding to the arguments.
#'
#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()].
#'
#' @examples
#' control_coxreg()
#'
#' @export
control_coxreg <- function(pval_method = c("wald", "likelihood"),
                           ties = c("exact", "efron", "breslow"),
                           conf_level = 0.95,
                           interaction = FALSE) {
  pval_method <- match.arg(pval_method)
  ties <- match.arg(ties)
  checkmate::assert_flag(interaction)
  assert_proportion_value(conf_level)
  list(
    pval_method = pval_method,
    ties = ties,
    conf_level = conf_level,
    interaction = interaction
  )
}

#' Custom tidy methods for Cox regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @param x (`list`)\cr result of the Cox regression model fitted by [fit_coxreg_univar()] (for univariate models)
#'   or [fit_coxreg_multivar()] (for multivariate models).
#'
#' @return [broom::tidy()] returns:
#' * For `summary.coxph` objects,  a `data.frame` with columns: `Pr(>|z|)`, `exp(coef)`, `exp(-coef)`, `lower .95`,
#'   `upper .95`, `level`, and `n`.
#' * For `coxreg.univar` objects, a `data.frame` with columns: `effect`, `term`, `term_label`, `level`, `n`, `hr`,
#'   `lcl`, `ucl`, `pval`, and `ci`.
#' * For `coxreg.multivar` objects, a `data.frame` with columns: `term`, `pval`, `term_label`, `hr`, `lcl`, `ucl`,
#'   `level`, and `ci`.
#'
#' @seealso [cox_regression]
#'
#' @name tidy_coxreg
NULL

#' @describeIn tidy_coxreg Custom tidy method for [survival::coxph()] summary results.
#'
#' Tidy the [survival::coxph()] results into a `data.frame` to extract model results.
#'
#' @method tidy summary.coxph
#'
#' @examples
#' library(survival)
#' library(broom)
#'
#' set.seed(1, kind = "Mersenne-Twister")
#'
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   data.frame(
#'     time = stop,
#'     status = event,
#'     armcd = as.factor(rx),
#'     covar1 = as.factor(enum),
#'     covar2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4, labels = c("F", "F", "M", "M")
#'     )
#'   )
#' )
#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#'
#' formula <- "survival::Surv(time, status) ~ armcd + covar1"
#' msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder))
#' tidy(msum)
#'
#' @export
tidy.summary.coxph <- function(x, # nolint
                               ...) {
  checkmate::assert_class(x, "summary.coxph")
  pval <- x$coefficients
  confint <- x$conf.int
  levels <- rownames(pval)

  pval <- tibble::as_tibble(pval)
  confint <- tibble::as_tibble(confint)

  ret <- cbind(pval[, grepl("Pr", names(pval))], confint)
  ret$level <- levels
  ret$n <- x[["n"]]
  ret
}

#' @describeIn tidy_coxreg Custom tidy method for a univariate Cox regression.
#'
#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_univar()].
#'
#' @method tidy coxreg.univar
#'
#' @examples
#' ## Cox regression: arm + 1 covariate.
#' mod1 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = "covar1"
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91)
#' )
#'
#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
#' mod2 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91, interaction = TRUE)
#' )
#'
#' tidy(mod1)
#' tidy(mod2)
#'
#' @export
tidy.coxreg.univar <- function(x, # nolint
                               ...) {
  checkmate::assert_class(x, "coxreg.univar")
  mod <- x$mod
  vars <- c(x$vars$arm, x$vars$covariates)
  has_arm <- "arm" %in% names(x$vars)

  result <- if (!has_arm) {
    Map(
      mod = mod, vars = vars,
      f = function(mod, vars) {
        h_coxreg_multivar_extract(
          var = vars,
          data = x$data,
          mod = mod,
          control = x$control
        )
      }
    )
  } else if (x$control$interaction) {
    Map(
      mod = mod, covar = vars,
      f = function(mod, covar) {
        h_coxreg_extract_interaction(
          effect = x$vars$arm, covar = covar, mod = mod, data = x$data,
          at = x$at, control = x$control
        )
      }
    )
  } else {
    Map(
      mod = mod, vars = vars,
      f = function(mod, vars) {
        h_coxreg_univar_extract(
          effect = x$vars$arm, covar = vars, data = x$data, mod = mod,
          control = x$control
        )
      }
    )
  }
  result <- do.call(rbind, result)

  result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
  result$n <- lapply(result$n, empty_vector_if_na)
  result$ci <- lapply(result$ci, empty_vector_if_na)
  result$hr <- lapply(result$hr, empty_vector_if_na)
  if (x$control$interaction) {
    result$pval_inter <- lapply(result$pval_inter, empty_vector_if_na)
    # Remove interaction p-values due to change in specifications.
    result$pval[result$effect != "Treatment:"] <- NA
  }
  result$pval <- lapply(result$pval, empty_vector_if_na)
  attr(result, "conf_level") <- x$control$conf_level
  result
}

#' @describeIn tidy_coxreg Custom tidy method for a multivariate Cox regression.
#'
#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_multivar()].
#'
#' @method tidy coxreg.multivar
#'
#' @examples
#' multivar_model <- fit_coxreg_multivar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#' broom::tidy(multivar_model)
#'
#' @export
tidy.coxreg.multivar <- function(x, # nolint
                                 ...) {
  checkmate::assert_class(x, "coxreg.multivar")
  vars <- c(x$vars$arm, x$vars$covariates)

  # Convert the model summaries to data.
  result <- Map(
    vars = vars,
    f = function(vars) {
      h_coxreg_multivar_extract(
        var = vars, data = x$data,
        mod = x$mod, control = x$control
      )
    }
  )
  result <- do.call(rbind, result)

  result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
  result$ci <- lapply(result$ci, empty_vector_if_na)
  result$hr <- lapply(result$hr, empty_vector_if_na)
  result$pval <- lapply(result$pval, empty_vector_if_na)
  result <- result[, names(result) != "n"]
  attr(result, "conf_level") <- x$control$conf_level

  result
}

#' Fitting functions for Cox proportional hazards regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fitting functions for univariate and multivariate Cox regression models.
#'
#' @param variables (named `list`)\cr the names of the variables found in `data`, passed as a named list and
#'   corresponding to the `time`, `event`, `arm`, `strata`, and `covariates` terms. If `arm` is missing from
#'   `variables`, then only Cox model(s) including the `covariates` will be fitted and the corresponding effect
#'   estimates will be tabulated later.
#' @param data (`data.frame`)\cr the dataset containing the variables to fit the models.
#' @param at (`list` of `numeric`)\cr when the candidate covariate is a `numeric`, use `at` to specify
#'   the value of the covariate at which the effect should be estimated.
#' @param control (`list`)\cr a list of parameters as returned by the helper function [control_coxreg()].
#'
#' @seealso [h_cox_regression] for relevant helper functions, [cox_regression].
#'
#' @examples
#' library(survival)
#'
#' set.seed(1, kind = "Mersenne-Twister")
#'
#' # Testing dataset [survival::bladder].
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   data.frame(
#'     time = stop,
#'     status = event,
#'     armcd = as.factor(rx),
#'     covar1 = as.factor(enum),
#'     covar2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4, labels = c("F", "F", "M", "M")
#'     )
#'   )
#' )
#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#'
#' plot(
#'   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
#'   lty = 2:4,
#'   xlab = "Months",
#'   col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
#' )
#'
#' @name fit_coxreg
NULL

#' @describeIn fit_coxreg Fit a series of univariate Cox regression models given the inputs.
#'
#' @return
#' * `fit_coxreg_univar()` returns a `coxreg.univar` class object which is a named `list`
#'   with 5 elements:
#'   * `mod`: Cox regression models fitted by [survival::coxph()].
#'   * `data`: The original data frame input.
#'   * `control`: The original control input.
#'   * `vars`: The variables used in the model.
#'   * `at`: Value of the covariate at which the effect should be estimated.
#'
#' @note When using `fit_coxreg_univar` there should be two study arms.
#'
#' @examples
#' # fit_coxreg_univar
#'
#' ## Cox regression: arm + 1 covariate.
#' mod1 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = "covar1"
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91)
#' )
#'
#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
#' mod2 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91, interaction = TRUE)
#' )
#'
#' ## Cox regression: arm + 1 covariate, stratified analysis.
#' mod3 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", strata = "covar2",
#'     covariates = c("covar1")
#'   ),
#'   data = dta_bladder,
#'   control = control_coxreg(conf_level = 0.91)
#' )
#'
#' ## Cox regression: no arm, only covariates.
#' mod4 <- fit_coxreg_univar(
#'   variables = list(
#'     time = "time", event = "status",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#'
#' @export
fit_coxreg_univar <- function(variables,
                              data,
                              at = list(),
                              control = control_coxreg()) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  assert_df_with_variables(data, variables)
  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  if (!is.null(variables$strata)) {
    checkmate::assert_disjunct(control$pval_method, "likelihood")
  }
  if (has_arm) {
    assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  }
  vars <- unlist(variables[c(arm_name, "covariates", "strata")], use.names = FALSE)
  for (i in vars) {
    if (is.factor(data[[i]])) {
      attr(data[[i]], "levels") <- levels(droplevels(data[[i]]))
    }
  }
  forms <- h_coxreg_univar_formulas(variables, interaction = control$interaction)
  mod <- lapply(
    forms, function(x) {
      survival::coxph(formula = stats::as.formula(x), data = data, ties = control$ties)
    }
  )
  structure(
    list(
      mod = mod,
      data = data,
      control = control,
      vars = variables,
      at = at
    ),
    class = "coxreg.univar"
  )
}

#' @describeIn fit_coxreg Fit a multivariate Cox regression model.
#'
#' @return
#' * `fit_coxreg_multivar()` returns a `coxreg.multivar` class object which is a named list
#'   with 4 elements:
#'   * `mod`: Cox regression model fitted by [survival::coxph()].
#'   * `data`: The original data frame input.
#'   * `control`: The original control input.
#'   * `vars`: The variables used in the model.
#'
#' @examples
#' # fit_coxreg_multivar
#'
#' ## Cox regression: multivariate Cox regression.
#' multivar_model <- fit_coxreg_multivar(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#'
#' # Example without treatment arm.
#' multivar_covs_model <- fit_coxreg_multivar(
#'   variables = list(
#'     time = "time", event = "status",
#'     covariates = c("covar1", "covar2")
#'   ),
#'   data = dta_bladder
#' )
#'
#' @export
fit_coxreg_multivar <- function(variables,
                                data,
                                control = control_coxreg()) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  if (!is.null(variables$covariates)) {
    checkmate::assert_character(variables$covariates)
  }

  checkmate::assert_false(control$interaction)
  assert_df_with_variables(data, variables)
  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  if (!is.null(variables$strata)) {
    checkmate::assert_disjunct(control$pval_method, "likelihood")
  }

  form <- h_coxreg_multivar_formula(variables)
  mod <- survival::coxph(
    formula = stats::as.formula(form),
    data = data,
    ties = control$ties
  )
  structure(
    list(
      mod = mod,
      data = data,
      control = control,
      vars = variables
    ),
    class = "coxreg.multivar"
  )
}

#' Muffled `car::Anova`
#'
#' Applied on survival models, [car::Anova()] signal that the `strata` terms is dropped from the model formula when
#' present, this function deliberately muffles this message.
#'
#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
#' @param test_statistic (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
#'
#' @return The output of [car::Anova()], with convergence message muffled.
#'
#' @keywords internal
muffled_car_anova <- function(mod, test_statistic) {
  tryCatch(
    withCallingHandlers(
      expr = {
        car::Anova(
          mod,
          test.statistic = test_statistic,
          type = "III"
        )
      },
      message = function(m) invokeRestart("muffleMessage"),
      error = function(e) {
        stop(paste(
          "the model seems to have convergence problems, please try to change",
          "the configuration of covariates or strata variables, e.g.",
          "- original error:", e
        ))
      }
    )
  )
}

#' Confidence interval for mean
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convenient function for calculating the mean confidence interval. It calculates the arithmetic as well as the
#' geometric mean. It can be used as a `ggplot` helper function for plotting.
#'
#' @inheritParams argument_convention
#' @param n_min (`numeric(1)`)\cr a minimum number of non-missing `x` to estimate the confidence interval for mean.
#' @param gg_helper (`flag`)\cr whether output should be aligned for use with `ggplot`s.
#' @param geom_mean (`flag`)\cr whether the geometric mean should be calculated.
#'
#' @return A named `vector` of values `mean_ci_lwr` and `mean_ci_upr`.
#'
#' @examples
#' stat_mean_ci(sample(10), gg_helper = FALSE)
#'
#' p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +
#'   ggplot2::geom_point()
#'
#' p + ggplot2::stat_summary(
#'   fun.data = stat_mean_ci,
#'   geom = "errorbar"
#' )
#'
#' p + ggplot2::stat_summary(
#'   fun.data = stat_mean_ci,
#'   fun.args = list(conf_level = 0.5),
#'   geom = "errorbar"
#' )
#'
#' p + ggplot2::stat_summary(
#'   fun.data = stat_mean_ci,
#'   fun.args = list(conf_level = 0.5, geom_mean = TRUE),
#'   geom = "errorbar"
#' )
#'
#' @export
stat_mean_ci <- function(x,
                         conf_level = 0.95,
                         na.rm = TRUE, # nolint
                         n_min = 2,
                         gg_helper = TRUE,
                         geom_mean = FALSE) {
  if (na.rm) {
    x <- stats::na.omit(x)
  }
  n <- length(x)

  if (!geom_mean) {
    m <- mean(x)
  } else {
    negative_values_exist <- any(is.na(x[!is.na(x)]) <- x[!is.na(x)] <= 0)
    if (negative_values_exist) {
      m <- NA_real_
    } else {
      x <- log(x)
      m <- mean(x)
    }
  }

  if (n < n_min || is.na(m)) {
    ci <- c(mean_ci_lwr = NA_real_, mean_ci_upr = NA_real_)
  } else {
    hci <- stats::qt((1 + conf_level) / 2, df = n - 1) * stats::sd(x) / sqrt(n)
    ci <- c(mean_ci_lwr = m - hci, mean_ci_upr = m + hci)
    if (geom_mean) {
      ci <- exp(ci)
    }
  }

  if (gg_helper) {
    m <- ifelse(is.na(m), NA_real_, m)
    ci <- data.frame(y = ifelse(geom_mean, exp(m), m), ymin = ci[[1]], ymax = ci[[2]])
  }

  return(ci)
}

#' Confidence interval for median
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convenient function for calculating the median confidence interval. It can be used as a `ggplot` helper
#' function for plotting.
#'
#' @inheritParams argument_convention
#' @param gg_helper (`flag`)\cr whether output should be aligned for use with `ggplot`s.
#'
#' @details This function was adapted from `DescTools/versions/0.99.35/source`
#'
#' @return A named `vector` of values `median_ci_lwr` and `median_ci_upr`.
#'
#' @examples
#' stat_median_ci(sample(10), gg_helper = FALSE)
#'
#' p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +
#'   ggplot2::geom_point()
#' p + ggplot2::stat_summary(
#'   fun.data = stat_median_ci,
#'   geom = "errorbar"
#' )
#'
#' @export
stat_median_ci <- function(x,
                           conf_level = 0.95,
                           na.rm = TRUE, # nolint
                           gg_helper = TRUE) {
  x <- unname(x)
  if (na.rm) {
    x <- x[!is.na(x)]
  }
  n <- length(x)
  med <- stats::median(x)

  k <- stats::qbinom(p = (1 - conf_level) / 2, size = n, prob = 0.5, lower.tail = TRUE)

  # k == 0 - for small samples (e.g. n <= 5) ci can be outside the observed range
  if (k == 0 || is.na(med)) {
    ci <- c(median_ci_lwr = NA_real_, median_ci_upr = NA_real_)
    empir_conf_level <- NA_real_
  } else {
    x_sort <- sort(x)
    ci <- c(median_ci_lwr = x_sort[k], median_ci_upr = x_sort[n - k + 1])
    empir_conf_level <- 1 - 2 * stats::pbinom(k - 1, size = n, prob = 0.5)
  }

  if (gg_helper) {
    ci <- data.frame(y = med, ymin = ci[[1]], ymax = ci[[2]])
  }

  attr(ci, "conf_level") <- empir_conf_level

  return(ci)
}

#' p-Value of the mean
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Convenient function for calculating the two-sided p-value of the mean.
#'
#' @inheritParams argument_convention
#' @param n_min (`numeric(1)`)\cr a minimum number of non-missing `x` to estimate the p-value of the mean.
#' @param test_mean (`numeric(1)`)\cr mean value to test under the null hypothesis.
#'
#' @return A p-value.
#'
#' @examples
#' stat_mean_pval(sample(10))
#'
#' stat_mean_pval(rnorm(10), test_mean = 0.5)
#'
#' @export
stat_mean_pval <- function(x,
                           na.rm = TRUE, # nolint
                           n_min = 2,
                           test_mean = 0) {
  if (na.rm) {
    x <- stats::na.omit(x)
  }
  n <- length(x)

  x_mean <- mean(x)
  x_sd <- stats::sd(x)

  if (n < n_min) {
    pv <- c(p_value = NA_real_)
  } else {
    x_se <- stats::sd(x) / sqrt(n)
    ttest <- (x_mean - test_mean) / x_se
    pv <- c(p_value = 2 * stats::pt(-abs(ttest), df = n - 1))
  }

  return(pv)
}

#' Proportion difference and confidence interval
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Function for calculating the proportion (or risk) difference and confidence interval between arm
#' X (reference group) and arm Y. Risk difference is calculated by subtracting cumulative incidence
#' in arm Y from cumulative incidence in arm X.
#'
#' @inheritParams argument_convention
#' @param x (`list` of `integer`)\cr list of number of occurrences in arm X (reference group).
#' @param y (`list` of `integer`)\cr list of number of occurrences in arm Y. Must be of equal length to `x`.
#' @param N_x (`numeric(1)`)\cr total number of records in arm X.
#' @param N_y (`numeric(1)`)\cr total number of records in arm Y.
#' @param list_names (`character`)\cr names of each variable/level corresponding to pair of proportions in
#'   `x` and `y`. Must be of equal length to `x` and `y`.
#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
#'
#' @return List of proportion differences and CIs corresponding to each pair of number of occurrences in `x` and
#'   `y`. Each list element consists of 3 statistics: proportion difference, CI lower bound, and CI upper bound.
#'
#' @seealso Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()]
#'   with `riskdiff` argument is set to `TRUE` in subsequent analyze functions, adds a column containing
#'   proportion (risk) difference to an `rtables` layout.
#'
#' @examples
#' stat_propdiff_ci(
#'   x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = "x", conf_level = 0.9
#' )
#'
#' stat_propdiff_ci(
#'   x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE
#' )
#'
#' @export
stat_propdiff_ci <- function(x,
                             y,
                             N_x, # nolint
                             N_y, # nolint
                             list_names = NULL,
                             conf_level = 0.95,
                             pct = TRUE) {
  checkmate::assert_list(x, types = "numeric")
  checkmate::assert_list(y, types = "numeric", len = length(x))
  checkmate::assert_character(list_names, len = length(x), null.ok = TRUE)
  rd_list <- lapply(seq_along(x), function(i) {
    p_x <- x[[i]] / N_x
    p_y <- y[[i]] / N_y
    rd_ci <- p_x - p_y + c(-1, 1) * stats::qnorm((1 + conf_level) / 2) *
      sqrt(p_x * (1 - p_x) / N_x + p_y * (1 - p_y) / N_y)
    c(p_x - p_y, rd_ci) * ifelse(pct, 100, 1)
  })
  names(rd_list) <- list_names
  rd_list
}

#' Helper functions for Cox proportional hazards regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions used in [fit_coxreg_univar()] and [fit_coxreg_multivar()].
#'
#' @inheritParams argument_convention
#' @inheritParams h_coxreg_univar_extract
#' @inheritParams cox_regression_inter
#' @inheritParams control_coxreg
#'
#' @seealso [cox_regression]
#'
#' @name h_cox_regression
NULL

#' @describeIn h_cox_regression Helper for Cox regression formula. Creates a list of formulas. It is used
#'   internally by [fit_coxreg_univar()] for the comparison of univariate Cox regression models.
#'
#' @return
#' * `h_coxreg_univar_formulas()` returns a `character` vector coercible into formulas (e.g [stats::as.formula()]).
#'
#' @examples
#' # `h_coxreg_univar_formulas`
#'
#' ## Simple formulas.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", covariates = c("X", "y")
#'   )
#' )
#'
#' ## Addition of an optional strata.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
#'     strata = "SITE"
#'   )
#' )
#'
#' ## Inclusion of the interaction term.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
#'     strata = "SITE"
#'   ),
#'   interaction = TRUE
#' )
#'
#' ## Only covariates fitted in separate models.
#' h_coxreg_univar_formulas(
#'   variables = list(
#'     time = "time", event = "status", covariates = c("X", "y")
#'   )
#' )
#'
#' @export
h_coxreg_univar_formulas <- function(variables,
                                     interaction = FALSE) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  checkmate::assert_flag(interaction)

  if (!has_arm || is.null(variables$covariates)) {
    checkmate::assert_false(interaction)
  }

  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  if (!is.null(variables$covariates)) {
    forms <- paste0(
      "survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
      ifelse(has_arm, variables$arm, "1"),
      ifelse(interaction, " * ", " + "),
      variables$covariates,
      ifelse(
        !is.null(variables$strata),
        paste0(" + strata(", paste0(variables$strata, collapse = ", "), ")"),
        ""
      )
    )
  } else {
    forms <- NULL
  }
  nams <- variables$covariates
  if (has_arm) {
    ref <- paste0(
      "survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
      variables$arm,
      ifelse(
        !is.null(variables$strata),
        paste0(
          " + strata(", paste0(variables$strata, collapse = ", "), ")"
        ),
        ""
      )
    )
    forms <- c(ref, forms)
    nams <- c("ref", nams)
  }
  stats::setNames(forms, nams)
}

#' @describeIn h_cox_regression Helper for multivariate Cox regression formula. Creates a formulas
#'   string. It is used internally by [fit_coxreg_multivar()] for the comparison of multivariate Cox
#'   regression models. Interactions will not be included in multivariate Cox regression model.
#'
#' @return
#' * `h_coxreg_multivar_formula()` returns a `string` coercible into a formula (e.g [stats::as.formula()]).
#'
#' @examples
#' # `h_coxreg_multivar_formula`
#'
#' h_coxreg_multivar_formula(
#'   variables = list(
#'     time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE")
#'   )
#' )
#'
#' # Addition of an optional strata.
#' h_coxreg_multivar_formula(
#'   variables = list(
#'     time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE"),
#'     strata = "SITE"
#'   )
#' )
#'
#' # Example without treatment arm.
#' h_coxreg_multivar_formula(
#'   variables = list(
#'     time = "AVAL", event = "event", covariates = c("RACE", "AGE"),
#'     strata = "SITE"
#'   )
#' )
#'
#' @export
h_coxreg_multivar_formula <- function(variables) {
  checkmate::assert_list(variables, names = "named")
  has_arm <- "arm" %in% names(variables)
  arm_name <- if (has_arm) "arm" else NULL

  checkmate::assert_character(variables$covariates, null.ok = TRUE)

  assert_list_of_variables(variables[c(arm_name, "event", "time")])

  y <- paste0(
    "survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
    ifelse(has_arm, variables$arm, "1")
  )
  if (length(variables$covariates) > 0) {
    y <- paste(y, paste(variables$covariates, collapse = " + "), sep = " + ")
  }
  if (!is.null(variables$strata)) {
    y <- paste0(y, " + strata(", paste0(variables$strata, collapse = ", "), ")")
  }
  y
}

#' @describeIn h_cox_regression Utility function to help tabulate the result of
#'   a univariate Cox regression model.
#'
#' @param effect (`string`)\cr the treatment variable.
#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
#'
#' @return
#' * `h_coxreg_univar_extract()` returns a `data.frame` with variables `effect`, `term`, `term_label`, `level`,
#'   `n`, `hr`, `lcl`, `ucl`, and `pval`.
#'
#' @examples
#' library(survival)
#'
#' dta_simple <- data.frame(
#'   time = c(5, 5, 10, 10, 5, 5, 10, 10),
#'   status = c(0, 0, 1, 0, 0, 1, 1, 1),
#'   armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c("A", "B")),
#'   var1 = c(45, 55, 65, 75, 55, 65, 85, 75),
#'   var2 = c("F", "M", "F", "M", "F", "M", "F", "U")
#' )
#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
#' result <- h_coxreg_univar_extract(
#'   effect = "armcd", covar = "armcd", mod = mod, data = dta_simple
#' )
#' result
#'
#' @export
h_coxreg_univar_extract <- function(effect,
                                    covar,
                                    data,
                                    mod,
                                    control = control_coxreg()) {
  checkmate::assert_string(covar)
  checkmate::assert_string(effect)
  checkmate::assert_class(mod, "coxph")
  test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]

  mod_aov <- muffled_car_anova(mod, test_statistic)
  msum <- summary(mod, conf.int = control$conf_level)
  sum_cox <- broom::tidy(msum)

  # Combine results together.
  effect_aov <- mod_aov[effect, , drop = TRUE]
  pval <- effect_aov[[grep(pattern = "Pr", x = names(effect_aov)), drop = TRUE]]
  sum_main <- sum_cox[grepl(effect, sum_cox$level), ]

  term_label <- if (effect == covar) {
    paste0(
      levels(data[[covar]])[2],
      " vs control (",
      levels(data[[covar]])[1],
      ")"
    )
  } else {
    unname(labels_or_names(data[covar]))
  }
  data.frame(
    effect = ifelse(covar == effect, "Treatment:", "Covariate:"),
    term = covar,
    term_label = term_label,
    level = levels(data[[effect]])[2],
    n = mod[["n"]],
    hr = unname(sum_main["exp(coef)"]),
    lcl = unname(sum_main[grep("lower", names(sum_main))]),
    ucl = unname(sum_main[grep("upper", names(sum_main))]),
    pval = pval,
    stringsAsFactors = FALSE
  )
}

#' @describeIn h_cox_regression Tabulation of multivariate Cox regressions. Utility function to help
#'   tabulate the result of a multivariate Cox regression model for a treatment/covariate variable.
#'
#' @return
#' * `h_coxreg_multivar_extract()` returns a `data.frame` with variables `pval`, `hr`, `lcl`, `ucl`, `level`,
#'   `n`, `term`, and `term_label`.
#'
#' @examples
#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
#' result <- h_coxreg_multivar_extract(
#'   var = "var1", mod = mod, data = dta_simple
#' )
#' result
#'
#' @export
h_coxreg_multivar_extract <- function(var,
                                      data,
                                      mod,
                                      control = control_coxreg()) {
  test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
  mod_aov <- muffled_car_anova(mod, test_statistic)

  msum <- summary(mod, conf.int = control$conf_level)
  sum_anova <- broom::tidy(mod_aov)
  sum_cox <- broom::tidy(msum)

  ret_anova <- sum_anova[sum_anova$term == var, c("term", "p.value")]
  names(ret_anova)[2] <- "pval"
  if (is.factor(data[[var]])) {
    ret_cox <- sum_cox[startsWith(prefix = var, x = sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
  } else {
    ret_cox <- sum_cox[(var == sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
  }
  names(ret_cox)[1:4] <- c("pval", "hr", "lcl", "ucl")
  varlab <- unname(labels_or_names(data[var]))
  ret_cox$term <- varlab

  if (is.numeric(data[[var]])) {
    ret <- ret_cox
    ret$term_label <- ret$term
  } else if (length(levels(data[[var]])) <= 2) {
    ret_anova$pval <- NA
    ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
    ret_cox$level <- gsub(var, "", ret_cox$level)
    ret_cox$term_label <- ret_cox$level
    ret <- dplyr::bind_rows(ret_anova, ret_cox)
  } else {
    ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
    ret_cox$level <- gsub(var, "", ret_cox$level)
    ret_cox$term_label <- ret_cox$level
    ret <- dplyr::bind_rows(ret_anova, ret_cox)
  }

  as.data.frame(ret)
}

#' Helper functions for tabulating binary response by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions that tabulate in a data frame statistics such as response rate
#' and odds ratio for population subgroups.
#'
#' @inheritParams argument_convention
#' @inheritParams response_subgroups
#' @param arm (`factor`)\cr the treatment group variable.
#'
#' @details Main functionality is to prepare data for use in a layout-creating function.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
#'   droplevels() %>%
#'   mutate(
#'     # Reorder levels of factor to make the placebo group the reference arm.
#'     ARM = fct_relevel(ARM, "B: Placebo"),
#'     rsp = AVALC == "CR"
#'   )
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' @name h_response_subgroups
NULL

#' @describeIn h_response_subgroups Helper to prepare a data frame of binary responses by arm.
#'
#' @return
#' * `h_proportion_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, and `prop`.
#'
#' @examples
#' h_proportion_df(
#'   c(TRUE, FALSE, FALSE),
#'   arm = factor(c("A", "A", "B"), levels = c("A", "B"))
#' )
#'
#' @export
h_proportion_df <- function(rsp, arm) {
  checkmate::assert_logical(rsp)
  assert_valid_factor(arm, len = length(rsp))
  non_missing_rsp <- !is.na(rsp)
  rsp <- rsp[non_missing_rsp]
  arm <- arm[non_missing_rsp]

  lst_rsp <- split(rsp, arm)
  lst_results <- Map(function(x, arm) {
    if (length(x) > 0) {
      s_prop <- s_proportion(df = x)
      data.frame(
        arm = arm,
        n = length(x),
        n_rsp = unname(s_prop$n_prop[1]),
        prop = unname(s_prop$n_prop[2]),
        stringsAsFactors = FALSE
      )
    } else {
      data.frame(
        arm = arm,
        n = 0L,
        n_rsp = NA,
        prop = NA,
        stringsAsFactors = FALSE
      )
    }
  }, lst_rsp, names(lst_rsp))

  df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
  df$arm <- factor(df$arm, levels = levels(arm))
  df
}

#' @describeIn h_response_subgroups Summarizes proportion of binary responses by arm and across subgroups
#'    in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
#'    requires elements `rsp`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
#'    groupings for `subgroups` variables.
#'
#' @return
#' * `h_proportion_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`,
#'   `var`, `var_label`, and `row_type`.
#'
#' @examples
#' h_proportion_subgroups_df(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f
#' )
#'
#' # Define groupings for BMRKR2 levels.
#' h_proportion_subgroups_df(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' @export
h_proportion_subgroups_df <- function(variables,
                                      data,
                                      groups_lists = list(),
                                      label_all = "All Patients") {
  checkmate::assert_character(variables$rsp)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  assert_df_with_variables(data, variables)
  checkmate::assert_string(label_all)

  # Add All Patients.
  result_all <- h_proportion_df(data[[variables$rsp]], data[[variables$arm]])
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  # Add Subgroups.
  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)

    l_result <- lapply(l_data, function(grp) {
      result <- h_proportion_df(grp$df[[variables$rsp]], grp$df[[variables$arm]])
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"

    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' @describeIn h_response_subgroups Helper to prepare a data frame with estimates of
#'   the odds ratio between a treatment and a control arm.
#'
#' @inheritParams response_subgroups
#' @param strata_data (`factor`, `data.frame`, or `NULL`)\cr required if stratified analysis is performed.
#'
#' @return
#' * `h_odds_ratio_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`, and
#'   optionally `pval` and `pval_label`.
#'
#' @examples
#' # Unstratatified analysis.
#' h_odds_ratio_df(
#'   c(TRUE, FALSE, FALSE, TRUE),
#'   arm = factor(c("A", "A", "B", "B"), levels = c("A", "B"))
#' )
#'
#' # Include p-value.
#' h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = "chisq")
#'
#' # Stratatified analysis.
#' h_odds_ratio_df(
#'   rsp = adrs_f$rsp,
#'   arm = adrs_f$ARM,
#'   strata_data = adrs_f[, c("STRATA1", "STRATA2")],
#'   method = "cmh"
#' )
#'
#' @export
h_odds_ratio_df <- function(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL) {
  assert_valid_factor(arm, n.levels = 2, len = length(rsp))

  df_rsp <- data.frame(
    rsp = rsp,
    arm = arm
  )

  if (!is.null(strata_data)) {
    strata_var <- interaction(strata_data, drop = TRUE)
    strata_name <- "strata"

    assert_valid_factor(strata_var, len = nrow(df_rsp))

    df_rsp[[strata_name]] <- strata_var
  } else {
    strata_name <- NULL
  }

  l_df <- split(df_rsp, arm)

  if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
    # Odds ratio and CI.
    result_odds_ratio <- s_odds_ratio(
      df = l_df[[2]],
      .var = "rsp",
      .ref_group = l_df[[1]],
      .in_ref_col = FALSE,
      .df_row = df_rsp,
      variables = list(arm = "arm", strata = strata_name),
      conf_level = conf_level
    )

    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = unname(result_odds_ratio$n_tot["n_tot"]),
      or = unname(result_odds_ratio$or_ci["est"]),
      lcl = unname(result_odds_ratio$or_ci["lcl"]),
      ucl = unname(result_odds_ratio$or_ci["ucl"]),
      conf_level = conf_level,
      stringsAsFactors = FALSE
    )

    if (!is.null(method)) {
      # Test for difference.
      result_test <- s_test_proportion_diff(
        df = l_df[[2]],
        .var = "rsp",
        .ref_group = l_df[[1]],
        .in_ref_col = FALSE,
        variables = list(strata = strata_name),
        method = method
      )

      df$pval <- as.numeric(result_test$pval)
      df$pval_label <- obj_label(result_test$pval)
    }

    # In those cases cannot go through the model so will obtain n_tot from data.
  } else if (
    (nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) ||
      (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
  ) {
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = sum(stats::complete.cases(df_rsp)),
      or = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      stringsAsFactors = FALSE
    )
    if (!is.null(method)) {
      df$pval <- NA
      df$pval_label <- NA
    }
  } else {
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = 0L,
      or = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      stringsAsFactors = FALSE
    )

    if (!is.null(method)) {
      df$pval <- NA
      df$pval_label <- NA
    }
  }

  df
}

#' @describeIn h_response_subgroups Summarizes estimates of the odds ratio between a treatment and a control
#'   arm across subgroups in a data frame. `variables` corresponds to the names of variables found in
#'   `data`, passed as a named list and requires elements `rsp`, `arm` and optionally `subgroups`
#'   and `strata`. `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @return
#' * `h_odds_ratio_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`,
#'   `conf_level`, `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @examples
#' # Unstratified analysis.
#' h_odds_ratio_subgroups_df(
#'   variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
#'   data = adrs_f
#' )
#'
#' # Stratified analysis.
#' h_odds_ratio_subgroups_df(
#'   variables = list(
#'     rsp = "rsp",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2"),
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adrs_f
#' )
#'
#' # Define groupings of BMRKR2 levels.
#' h_odds_ratio_subgroups_df(
#'   variables = list(
#'     rsp = "rsp",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adrs_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' @export
h_odds_ratio_subgroups_df <- function(variables,
                                      data,
                                      groups_lists = list(),
                                      conf_level = 0.95,
                                      method = NULL,
                                      label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_odds_ratio_subgroups_df() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  checkmate::assert_character(variables$rsp)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_character(variables$strata, null.ok = TRUE)
  assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  assert_df_with_variables(data, variables)
  checkmate::assert_string(label_all)

  strata_data <- if (is.null(variables$strata)) {
    NULL
  } else {
    data[, variables$strata, drop = FALSE]
  }

  # Add All Patients.
  result_all <- h_odds_ratio_df(
    rsp = data[[variables$rsp]],
    arm = data[[variables$arm]],
    strata_data = strata_data,
    conf_level = conf_level,
    method = method
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)

    l_result <- lapply(l_data, function(grp) {
      grp_strata_data <- if (is.null(variables$strata)) {
        NULL
      } else {
        grp$df[, variables$strata, drop = FALSE]
      }

      result <- h_odds_ratio_df(
        rsp = grp$df[[variables$rsp]],
        arm = grp$df[[variables$arm]],
        strata_data = grp_strata_data,
        conf_level = conf_level,
        method = method
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })

    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"

    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' Occurrence table pruning
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Family of constructor and condition functions to flexibly prune occurrence tables.
#' The condition functions always return whether the row result is higher than the threshold.
#' Since they are of class [CombinationFunction()] they can be logically combined with other condition
#' functions.
#'
#' @note Since most table specifications are worded positively, we name our constructor and condition
#'   functions positively, too. However, note that the result of [keep_rows()] says what
#'   should be pruned, to conform with the [rtables::prune_table()] interface.
#'
#' @examples
#' \donttest{
#' tab <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("RACE") %>%
#'   split_rows_by("STRATA1") %>%
#'   summarize_row_groups() %>%
#'   analyze_vars("COUNTRY", .stats = "count_fraction") %>%
#'   build_table(DM)
#' }
#'
#' @name prune_occurrences
NULL

#' @describeIn prune_occurrences Constructor for creating pruning functions based on
#'   a row condition function. This removes all analysis rows (`TableRow`) that should be
#'   pruned, i.e., don't fulfill the row condition. It removes the sub-tree if there are no
#'   children left.
#'
#' @param row_condition (`CombinationFunction`)\cr condition function which works on individual
#'   analysis rows and flags whether these should be kept in the pruned table.
#'
#' @return
#' * `keep_rows()` returns a pruning function that can be used with [rtables::prune_table()]
#'   to prune an `rtables` table.
#'
#' @examples
#' \donttest{
#' # `keep_rows`
#' is_non_empty <- !CombinationFunction(all_zero_or_na)
#' prune_table(tab, keep_rows(is_non_empty))
#' }
#'
#' @export
keep_rows <- function(row_condition) {
  checkmate::assert_function(row_condition)
  function(table_tree) {
    if (inherits(table_tree, "TableRow")) {
      return(!row_condition(table_tree))
    }
    children <- tree_children(table_tree)
    identical(length(children), 0L)
  }
}

#' @describeIn prune_occurrences Constructor for creating pruning functions based on
#'   a condition for the (first) content row in leaf tables. This removes all leaf tables where
#'   the first content row does not fulfill the condition. It does not check individual rows.
#'   It then proceeds recursively by removing the sub tree if there are no children left.
#'
#' @param content_row_condition (`CombinationFunction`)\cr condition function which works on individual
#'   first content rows of leaf tables and flags whether these leaf tables should be kept in the pruned table.
#'
#' @return
#' * `keep_content_rows()` returns a pruning function that checks the condition on the first content
#'   row of leaf tables in the table.
#'
#' @examples
#' # `keep_content_rows`
#' \donttest{
#' more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab))
#' prune_table(tab, keep_content_rows(more_than_twenty))
#' }
#'
#' @export
keep_content_rows <- function(content_row_condition) {
  checkmate::assert_function(content_row_condition)
  function(table_tree) {
    if (is_leaf_table(table_tree)) {
      content_row <- h_content_first_row(table_tree)
      return(!content_row_condition(content_row))
    }
    if (inherits(table_tree, "DataRow")) {
      return(FALSE)
    }
    children <- tree_children(table_tree)
    identical(length(children), 0L)
  }
}

#' @describeIn prune_occurrences Constructor for creating condition functions on total counts in the specified columns.
#'
#' @param atleast (`numeric(1)`)\cr threshold which should be met in order to keep the row.
#' @param ... arguments for row or column access, see [`rtables_access`]: either `col_names` (`character`) including
#'   the names of the columns which should be used, or alternatively `col_indices` (`integer`) giving the indices
#'   directly instead.
#'
#' @return
#' * `has_count_in_cols()` returns a condition function that sums the counts in the specified column.
#'
#' @examples
#' \donttest{
#' more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_one))
#' }
#'
#' @export
has_count_in_cols <- function(atleast, ...) {
  checkmate::assert_count(atleast)
  CombinationFunction(function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    total_count <- sum(row_counts)
    total_count >= atleast
  })
}

#' @describeIn prune_occurrences Constructor for creating condition functions on any of the counts in
#'   the specified columns satisfying a threshold.
#'
#' @param atleast (`numeric(1)`)\cr threshold which should be met in order to keep the row.
#'
#' @return
#' * `has_count_in_any_col()` returns a condition function that compares the counts in the
#'   specified columns with the threshold.
#'
#' @examples
#' \donttest{
#' # `has_count_in_any_col`
#' any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab))
#' prune_table(tab, keep_rows(any_more_than_one))
#' }
#'
#' @export
has_count_in_any_col <- function(atleast, ...) {
  checkmate::assert_count(atleast)
  CombinationFunction(function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    any(row_counts >= atleast)
  })
}

#' @describeIn prune_occurrences Constructor for creating condition functions on total fraction in
#'   the specified columns.
#'
#' @return
#' * `has_fraction_in_cols()` returns a condition function that sums the counts in the
#'   specified column, and computes the fraction by dividing by the total column counts.
#'
#' @examples
#' \donttest{
#' # `has_fraction_in_cols`
#' more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_five_percent))
#' }
#'
#' @export
has_fraction_in_cols <- function(atleast, ...) {
  assert_proportion_value(atleast, include_boundaries = TRUE)
  CombinationFunction(function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    total_count <- sum(row_counts)
    col_counts <- h_col_counts(table_row, ...)
    total_n <- sum(col_counts)
    total_percent <- total_count / total_n
    total_percent >= atleast
  })
}

#' @describeIn prune_occurrences Constructor for creating condition functions on any fraction in
#'   the specified columns.
#'
#' @return
#' * `has_fraction_in_any_col()` returns a condition function that looks at the fractions
#'  in the specified columns and checks whether any of them fulfill the threshold.
#'
#' @examples
#' \donttest{
#' # `has_fraction_in_any_col`
#' any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab))
#' prune_table(tab, keep_rows(any_atleast_five_percent))
#' }
#'
#' @export
has_fraction_in_any_col <- function(atleast, ...) {
  assert_proportion_value(atleast, include_boundaries = TRUE)
  CombinationFunction(function(table_row) {
    row_fractions <- h_row_fractions(table_row, ...)
    any(row_fractions >= atleast)
  })
}

#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
#'   between the fractions reported in each specified column.
#'
#' @return
#' * `has_fractions_difference()` returns a condition function that extracts the fractions of each
#'   specified column, and computes the difference of the minimum and maximum.
#'
#' @examples
#' \donttest{
#' # `has_fractions_difference`
#' more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_five_percent_diff))
#' }
#'
#' @export
has_fractions_difference <- function(atleast, ...) {
  assert_proportion_value(atleast, include_boundaries = TRUE)
  CombinationFunction(function(table_row) {
    fractions <- h_row_fractions(table_row, ...)
    difference <- diff(range(fractions))
    difference >= atleast
  })
}

#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
#'   between the counts reported in each specified column.
#'
#' @return
#' * `has_counts_difference()` returns a condition function that extracts the counts of each
#'   specified column, and computes the difference of the minimum and maximum.
#'
#' @examples
#' \donttest{
#' more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab))
#' prune_table(tab, keep_rows(more_than_one_diff))
#' }
#'
#' @export
has_counts_difference <- function(atleast, ...) {
  checkmate::assert_count(atleast)
  CombinationFunction(function(table_row) {
    counts <- h_row_counts(table_row, ...)
    difference <- diff(range(counts))
    difference >= atleast
  })
}

#' Re-implemented `range()` default S3 method for numerical objects
#'
#' This function returns `c(NA, NA)` instead of `c(-Inf, Inf)` for zero-length data
#' without any warnings.
#'
#' @param x (`numeric`)\cr a sequence of numbers for which the range is computed.
#' @param na.rm (`flag`)\cr flag indicating if `NA` should be omitted.
#' @param finite (`flag`)\cr flag indicating if non-finite elements should be removed.
#'
#' @return A 2-element vector of class `numeric`.
#'
#' @keywords internal
range_noinf <- function(x, na.rm = FALSE, finite = FALSE) { # nolint

  checkmate::assert_numeric(x)

  if (finite) {
    x <- x[is.finite(x)] # removes NAs too
  } else if (na.rm) {
    x <- x[!is.na(x)]
  }

  if (length(x) == 0) {
    rval <- c(NA, NA)
    mode(rval) <- typeof(x)
  } else {
    rval <- c(min(x, na.rm = FALSE), max(x, na.rm = FALSE))
  }

  return(rval)
}

#' Utility function to create label for confidence interval
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#'
#' @return A `string`.
#'
#' @export
f_conf_level <- function(conf_level) {
  assert_proportion_value(conf_level)
  paste0(conf_level * 100, "% CI")
}

#' Utility function to create label for p-value
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param test_mean (`numeric(1)`)\cr mean value to test under the null hypothesis.
#'
#' @return A `string`.
#'
#' @export
f_pval <- function(test_mean) {
  checkmate::assert_numeric(test_mean, len = 1)
  paste0("p-value (H0: mean = ", test_mean, ")")
}

#' Utility function to return a named list of covariate names
#'
#' @param covariates (`character`)\cr a vector that can contain single variable names (such as
#'   `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
#'
#' @return A named `list` of `character` vector.
#'
#' @keywords internal
get_covariates <- function(covariates) {
  checkmate::assert_character(covariates)
  cov_vars <- unique(trimws(unlist(strsplit(covariates, "\\*"))))
  stats::setNames(as.list(cov_vars), cov_vars)
}

#' Replicate entries of a vector if required
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Replicate entries of a vector if required.
#'
#' @inheritParams argument_convention
#' @param n (`integer(1)`)\cr number of entries that are needed.
#'
#' @return `x` if it has the required length already or is `NULL`,
#'   otherwise if it is scalar the replicated version of it with `n` entries.
#'
#' @note This function will fail if `x` is not of length `n` and/or is not a scalar.
#'
#' @export
to_n <- function(x, n) {
  if (is.null(x)) {
    NULL
  } else if (length(x) == 1) {
    rep(x, n)
  } else if (length(x) == n) {
    x
  } else {
    stop("dimension mismatch")
  }
}

#' Check element dimension
#'
#' Checks if the elements in `...` have the same dimension.
#'
#' @param ... (`data.frame` or `vector`)\cr any data frames or vectors.
#' @param omit_null (`flag`)\cr whether `NULL` elements in `...` should be omitted from the check.
#'
#' @return A `logical` value.
#'
#' @keywords internal
check_same_n <- function(..., omit_null = TRUE) {
  dots <- list(...)

  n_list <- Map(
    function(x, name) {
      if (is.null(x)) {
        if (omit_null) {
          NA_integer_
        } else {
          stop("arg", name, "is not supposed to be NULL")
        }
      } else if (is.data.frame(x)) {
        nrow(x)
      } else if (is.atomic(x)) {
        length(x)
      } else {
        stop("data structure for ", name, "is currently not supported")
      }
    },
    dots, names(dots)
  )

  n <- stats::na.omit(unlist(n_list))

  if (length(unique(n)) > 1) {
    sel <- which(n != n[1])
    stop("Dimension mismatch:", paste(names(n)[sel], collapse = ", "), " do not have N=", n[1])
  }

  TRUE
}

#' Utility function to check if a float value is equal to another float value
#'
#' Uses `.Machine$double.eps` as the tolerance for the comparison.
#'
#' @param x (`numeric(1)`)\cr a float number.
#' @param y (`numeric(1)`)\cr a float number.
#'
#' @return `TRUE` if identical, otherwise `FALSE`.
#'
#' @keywords internal
.is_equal_float <- function(x, y) {
  checkmate::assert_number(x)
  checkmate::assert_number(y)

  # Define a tolerance
  tolerance <- .Machine$double.eps

  # Check if x is close enough to y
  abs(x - y) < tolerance
}

#' Make names without dots
#'
#' @param nams (`character`)\cr vector of original names.
#'
#' @return A `character` `vector` of proper names, which does not use dots in contrast to [make.names()].
#'
#' @keywords internal
make_names <- function(nams) {
  orig <- make.names(nams)
  gsub(".", "", x = orig, fixed = TRUE)
}

#' Conversion of months to days
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Conversion of months to days. This is an approximative calculation because it
#' considers each month as having an average of 30.4375 days.
#'
#' @param x (`numeric(1)`)\cr time in months.
#'
#' @return A `numeric` vector with the time in days.
#'
#' @examples
#' x <- c(13.25, 8.15, 1, 2.834)
#' month2day(x)
#'
#' @export
month2day <- function(x) {
  checkmate::assert_numeric(x)
  x * 30.4375
}

#' Conversion of days to months
#'
#' @param x (`numeric(1)`)\cr time in days.
#'
#' @return A `numeric` vector with the time in months.
#'
#' @examples
#' x <- c(403, 248, 30, 86)
#' day2month(x)
#'
#' @export
day2month <- function(x) {
  checkmate::assert_numeric(x)
  x / 30.4375
}

#' Return an empty numeric if all elements are `NA`.
#'
#' @param x (`numeric`)\cr vector.
#'
#' @return An empty `numeric` if all elements of `x` are `NA`, otherwise `x`.
#'
#' @examples
#' x <- c(NA, NA, NA)
#' # Internal function - empty_vector_if_na
#' @keywords internal
empty_vector_if_na <- function(x) {
  if (all(is.na(x))) {
    numeric()
  } else {
    x
  }
}

#' Element-wise combination of two vectors
#'
#' @param x (`vector`)\cr first vector to combine.
#' @param y (`vector`)\cr second vector to combine.
#'
#' @return A `list` where each element combines corresponding elements of `x` and `y`.
#'
#' @examples
#' combine_vectors(1:3, 4:6)
#'
#' @export
combine_vectors <- function(x, y) {
  checkmate::assert_vector(x)
  checkmate::assert_vector(y, len = length(x))

  result <- lapply(as.data.frame(rbind(x, y)), `c`)
  names(result) <- NULL
  result
}

#' Extract elements by name
#'
#' This utility function extracts elements from a vector `x` by `names`.
#' Differences to the standard `[` function are:
#'
#' - If `x` is `NULL`, then still always `NULL` is returned (same as in base function).
#' - If `x` is not `NULL`, then the intersection of its names is made with `names` and those
#'   elements are returned. That is, `names` which don't appear in `x` are not returned as `NA`s.
#'
#' @param x (named `vector`)\cr where to extract named elements from.
#' @param names (`character`)\cr vector of names to extract.
#'
#' @return `NULL` if `x` is `NULL`, otherwise the extracted elements from `x`.
#'
#' @keywords internal
extract_by_name <- function(x, names) {
  if (is.null(x)) {
    return(NULL)
  }
  checkmate::assert_named(x)
  checkmate::assert_character(names)
  which_extract <- intersect(names(x), names)
  if (length(which_extract) > 0) {
    x[which_extract]
  } else {
    NULL
  }
}

#' Labels for adverse event baskets
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param aesi (`character`)\cr vector with standardized MedDRA query name (e.g. `SMQxxNAM`) or customized query
#'   name (e.g. `CQxxNAM`).
#' @param scope (`character`)\cr vector with scope of query (e.g. `SMQxxSC`).
#'
#' @return A `string` with the standard label for the AE basket.
#'
#' @examples
#' adae <- tern_ex_adae
#'
#' # Standardized query label includes scope.
#' aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC)
#'
#' # Customized query label.
#' aesi_label(adae$CQ01NAM)
#'
#' @export
aesi_label <- function(aesi, scope = NULL) {
  checkmate::assert_character(aesi)
  checkmate::assert_character(scope, null.ok = TRUE)
  aesi_label <- obj_label(aesi)
  aesi <- sas_na(aesi)
  aesi <- unique(aesi)[!is.na(unique(aesi))]

  lbl <- if (length(aesi) == 1 && !is.null(scope)) {
    scope <- sas_na(scope)
    scope <- unique(scope)[!is.na(unique(scope))]
    checkmate::assert_string(scope)
    paste0(aesi, " (", scope, ")")
  } else if (length(aesi) == 1 && is.null(scope)) {
    aesi
  } else {
    aesi_label
  }

  lbl
}

#' Indicate study arm variable in formula
#'
#' We use `study_arm` to indicate the study arm variable in `tern` formulas.
#'
#' @param x arm information
#'
#' @return `x`
#'
#' @keywords internal
study_arm <- function(x) {
  structure(x, varname = deparse(substitute(x)))
}

#' Smooth function with optional grouping
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This produces `loess` smoothed estimates of `y` with Student confidence intervals.
#'
#' @param df (`data.frame`)\cr data set containing all analysis variables.
#' @param x (`string`)\cr x column name.
#' @param y (`string`)\cr y column name.
#' @param groups (`character` or `NULL`)\cr vector with optional grouping variables names.
#' @param level (`proportion`)\cr level of confidence interval to use (0.95 by default).
#'
#' @return A `data.frame` with original `x`, smoothed `y`, `ylow`, and `yhigh`, and
#'   optional `groups` variables formatted as `factor` type.
#'
#' @export
get_smooths <- function(df, x, y, groups = NULL, level = 0.95) {
  checkmate::assert_data_frame(df)
  df_cols <- colnames(df)
  checkmate::assert_string(x)
  checkmate::assert_subset(x, df_cols)
  checkmate::assert_numeric(df[[x]])
  checkmate::assert_string(y)
  checkmate::assert_subset(y, df_cols)
  checkmate::assert_numeric(df[[y]])

  if (!is.null(groups)) {
    checkmate::assert_character(groups)
    checkmate::assert_subset(groups, df_cols)
  }

  smooths <- function(x, y) {
    stats::predict(stats::loess(y ~ x), se = TRUE)
  }

  if (!is.null(groups)) {
    cc <- stats::complete.cases(df[c(x, y, groups)])
    df_c <- df[cc, c(x, y, groups)]
    df_c_ordered <- df_c[do.call("order", as.list(df_c[, groups, drop = FALSE])), , drop = FALSE]
    df_c_g <- data.frame(Map(as.factor, df_c_ordered[groups]))

    df_smooth_raw <-
      by(df_c_ordered, df_c_g, function(d) {
        plx <- smooths(d[[x]], d[[y]])
        data.frame(
          x = d[[x]],
          y = plx$fit,
          ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
          yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
        )
      })

    df_smooth <- do.call(rbind, df_smooth_raw)
    df_smooth[groups] <- df_c_g

    df_smooth
  } else {
    cc <- stats::complete.cases(df[c(x, y)])
    df_c <- df[cc, ]
    plx <- smooths(df_c[[x]], df_c[[y]])

    df_smooth <- data.frame(
      x = df_c[[x]],
      y = plx$fit,
      ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
      yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
    )

    df_smooth
  }
}

#' Number of available (non-missing entries) in a vector
#'
#' Small utility function for better readability.
#'
#' @param x (`vector`)\cr vector in which to count non-missing values.
#'
#' @return Number of non-missing values.
#'
#' @keywords internal
n_available <- function(x) {
  sum(!is.na(x))
}

#' Reapply variable labels
#'
#' This is a helper function that is used in tests.
#'
#' @param x (`vector`)\cr vector of elements that needs new labels.
#' @param varlabels (`character`)\cr vector of labels for `x`.
#' @param ... further parameters to be added to the list.
#'
#' @return `x` with variable labels reapplied.
#'
#' @export
reapply_varlabels <- function(x, varlabels, ...) {
  named_labels <- c(as.list(varlabels), list(...))
  formatters::var_labels(x)[names(named_labels)] <- as.character(named_labels)
  x
}

# Wrapper function of survival::clogit so that when model fitting failed, a more useful message would show
clogit_with_tryCatch <- function(formula, data, ...) { # nolint
  tryCatch(
    survival::clogit(formula = formula, data = data, ...),
    error = function(e) stop("model not built successfully with survival::clogit")
  )
}

#' Convert list of groups to a data frame
#'
#' This converts a list of group levels into a data frame format which is expected by [rtables::add_combo_levels()].
#'
#' @param groups_list (named `list` of `character`)\cr specifies the new group levels via the names and the
#'   levels that belong to it in the character vectors that are elements of the list.
#'
#' @return A `tibble` in the required format.
#'
#' @examples
#' grade_groups <- list(
#'   "Any Grade (%)" = c("1", "2", "3", "4", "5"),
#'   "Grade 3-4 (%)" = c("3", "4"),
#'   "Grade 5 (%)" = "5"
#' )
#' groups_list_to_df(grade_groups)
#'
#' @export
groups_list_to_df <- function(groups_list) {
  checkmate::assert_list(groups_list, names = "named")
  lapply(groups_list, checkmate::assert_character)
  tibble::tibble(
    valname = make_names(names(groups_list)),
    label = names(groups_list),
    levelcombo = unname(groups_list),
    exargs = replicate(length(groups_list), list())
  )
}

#' Reference and treatment group combination
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Facilitate the re-combination of groups divided as reference and treatment groups; it helps in arranging groups of
#' columns in the `rtables` framework and teal modules.
#'
#' @param fct (`factor`)\cr the variable with levels which needs to be grouped.
#' @param ref (`character`)\cr the reference level(s).
#' @param collapse (`string`)\cr a character string to separate `fct` and `ref`.
#'
#' @return A `list` with first item `ref` (reference) and second item `trt` (treatment).
#'
#' @examples
#' groups <- combine_groups(
#'   fct = DM$ARM,
#'   ref = c("B: Placebo")
#' )
#'
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze_vars("AGE") %>%
#'   build_table(DM)
#'
#' @export
combine_groups <- function(fct,
                           ref = NULL,
                           collapse = "/") {
  checkmate::assert_string(collapse)
  checkmate::assert_character(ref, min.chars = 1, any.missing = FALSE, null.ok = TRUE)
  checkmate::assert_multi_class(fct, classes = c("factor", "character"))

  fct <- as_factor_keep_attributes(fct)

  group_levels <- levels(fct)
  if (is.null(ref)) {
    ref <- group_levels[1]
  } else {
    checkmate::assert_subset(ref, group_levels)
  }

  groups <- list(
    ref = group_levels[group_levels %in% ref],
    trt = group_levels[!group_levels %in% ref]
  )
  stats::setNames(groups, nm = lapply(groups, paste, collapse = collapse))
}

#' Split columns by groups of levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @inheritParams groups_list_to_df
#' @param ... additional arguments to [rtables::split_cols_by()] in order. For instance, to
#'   control formats (`format`), add a joint column for all groups (`incl_all`).
#'
#' @return A layout object suitable for passing to further layouting functions. Adding
#'   this function to an `rtable` layout will add a column split including the given
#'   groups to the table layout.
#'
#' @seealso [rtables::split_cols_by()]
#'
#' @examples
#' # 1 - Basic use
#'
#' # Without group combination `split_cols_by_groups` is
#' # equivalent to [rtables::split_cols_by()].
#' basic_table() %>%
#'   split_cols_by_groups("ARM") %>%
#'   add_colcounts() %>%
#'   analyze("AGE") %>%
#'   build_table(DM)
#'
#' # Add a reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", ref_group = "B: Placebo") %>%
#'   add_colcounts() %>%
#'   analyze(
#'     "AGE",
#'     afun = function(x, .ref_group, .in_ref_col) {
#'       if (.in_ref_col) {
#'         in_rows("Diff Mean" = rcell(NULL))
#'       } else {
#'         in_rows("Diff Mean" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#'       }
#'     }
#'   ) %>%
#'   build_table(DM)
#'
#' # 2 - Adding group specification
#'
#' # Manual preparation of the groups.
#' groups <- list(
#'   "Arms A+B" = c("A: Drug X", "B: Placebo"),
#'   "Arms A+C" = c("A: Drug X", "C: Combination")
#' )
#'
#' # Use of split_cols_by_groups without reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze("AGE") %>%
#'   build_table(DM)
#'
#' # Including differentiated output in the reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups_list = groups, ref_group = "Arms A+B") %>%
#'   analyze(
#'     "AGE",
#'     afun = function(x, .ref_group, .in_ref_col) {
#'       if (.in_ref_col) {
#'         in_rows("Diff. of Averages" = rcell(NULL))
#'       } else {
#'         in_rows("Diff. of Averages" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#'       }
#'     }
#'   ) %>%
#'   build_table(DM)
#'
#' # 3 - Binary list dividing factor levels into reference and treatment
#'
#' # `combine_groups` defines reference and treatment.
#' groups <- combine_groups(
#'   fct = DM$ARM,
#'   ref = c("A: Drug X", "B: Placebo")
#' )
#' groups
#'
#' # Use group definition without reference column.
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups_list = groups) %>%
#'   add_colcounts() %>%
#'   analyze("AGE") %>%
#'   build_table(DM)
#'
#' # Use group definition with reference column (first item of groups).
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups, ref_group = names(groups)[1]) %>%
#'   add_colcounts() %>%
#'   analyze(
#'     "AGE",
#'     afun = function(x, .ref_group, .in_ref_col) {
#'       if (.in_ref_col) {
#'         in_rows("Diff Mean" = rcell(NULL))
#'       } else {
#'         in_rows("Diff Mean" = rcell(mean(x) - mean(.ref_group), format = "xx.xx"))
#'       }
#'     }
#'   ) %>%
#'   build_table(DM)
#'
#' @export
split_cols_by_groups <- function(lyt,
                                 var,
                                 groups_list = NULL,
                                 ref_group = NULL,
                                 ...) {
  if (is.null(groups_list)) {
    split_cols_by(
      lyt = lyt,
      var = var,
      ref_group = ref_group,
      ...
    )
  } else {
    groups_df <- groups_list_to_df(groups_list)
    if (!is.null(ref_group)) {
      ref_group <- groups_df$valname[groups_df$label == ref_group]
    }
    split_cols_by(
      lyt = lyt,
      var = var,
      split_fun = add_combo_levels(groups_df, keep_levels = groups_df$valname),
      ref_group = ref_group,
      ...
    )
  }
}

#' Combine counts
#'
#' Simplifies the estimation of column counts, especially when group combination is required.
#'
#' @inheritParams combine_groups
#' @inheritParams groups_list_to_df
#'
#' @return A `vector` of column counts.
#'
#' @seealso [combine_groups()]
#'
#' @examples
#' ref <- c("A: Drug X", "B: Placebo")
#' groups <- combine_groups(fct = DM$ARM, ref = ref)
#'
#' col_counts <- combine_counts(
#'   fct = DM$ARM,
#'   groups_list = groups
#' )
#'
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze_vars("AGE") %>%
#'   build_table(DM, col_counts = col_counts)
#'
#' ref <- "A: Drug X"
#' groups <- combine_groups(fct = DM$ARM, ref = ref)
#' col_counts <- combine_counts(
#'   fct = DM$ARM,
#'   groups_list = groups
#' )
#'
#' basic_table() %>%
#'   split_cols_by_groups("ARM", groups) %>%
#'   add_colcounts() %>%
#'   analyze_vars("AGE") %>%
#'   build_table(DM, col_counts = col_counts)
#'
#' @export
combine_counts <- function(fct, groups_list = NULL) {
  checkmate::assert_multi_class(fct, classes = c("factor", "character"))

  fct <- as_factor_keep_attributes(fct)

  if (is.null(groups_list)) {
    y <- table(fct)
    y <- stats::setNames(as.numeric(y), nm = dimnames(y)[[1]])
  } else {
    y <- vapply(
      X = groups_list,
      FUN = function(x) sum(table(fct)[x]),
      FUN.VALUE = 1
    )
  }
  y
}

#' Helper functions for tabulating survival duration by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions that tabulate in a data frame statistics such as median survival
#' time and hazard ratio for population subgroups.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_coxph_pairwise
#' @inheritParams survival_duration_subgroups
#' @param arm (`factor`)\cr the treatment group variable.
#'
#' @details Main functionality is to prepare data for use in a layout-creating function.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte)
#'
#' adtte_f <- adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X"),
#'     SEX %in% c("M", "F")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
#'     SEX = droplevels(SEX),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("ARM" = adtte_labels[["ARM"]], "SEX" = adtte_labels[["SEX"]], "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' @name h_survival_duration_subgroups
NULL

#' @describeIn h_survival_duration_subgroups Helper to prepare a data frame of median survival times by arm.
#'
#' @return
#' * `h_survtime_df()` returns a `data.frame` with columns `arm`, `n`, `n_events`, and `median`.
#'
#' @examples
#' # Extract median survival time for one group.
#' h_survtime_df(
#'   tte = adtte_f$AVAL,
#'   is_event = adtte_f$is_event,
#'   arm = adtte_f$ARM
#' )
#'
#' @export
h_survtime_df <- function(tte, is_event, arm) {
  checkmate::assert_numeric(tte)
  checkmate::assert_logical(is_event, len = length(tte))
  assert_valid_factor(arm, len = length(tte))

  df_tte <- data.frame(
    tte = tte,
    is_event = is_event,
    stringsAsFactors = FALSE
  )

  # Delete NAs
  non_missing_rows <- stats::complete.cases(df_tte)
  df_tte <- df_tte[non_missing_rows, ]
  arm <- arm[non_missing_rows]

  lst_tte <- split(df_tte, arm)
  lst_results <- Map(function(x, arm) {
    if (nrow(x) > 0) {
      s_surv <- s_surv_time(x, .var = "tte", is_event = "is_event")
      median_est <- unname(as.numeric(s_surv$median))
      n_events <- sum(x$is_event)
    } else {
      median_est <- NA
      n_events <- NA
    }

    data.frame(
      arm = arm,
      n = nrow(x),
      n_events = n_events,
      median = median_est,
      stringsAsFactors = FALSE
    )
  }, lst_tte, names(lst_tte))

  df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
  df$arm <- factor(df$arm, levels = levels(arm))
  df
}

#' @describeIn h_survival_duration_subgroups Summarizes median survival times by arm and across subgroups
#'    in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
#'    requires elements `tte`, `is_event`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
#'    groupings for `subgroups` variables.
#'
#' @return
#' * `h_survtime_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_events`, `median`, `subgroup`,
#'   `var`, `var_label`, and `row_type`.
#'
#' @examples
#' # Extract median survival time for multiple groups.
#' h_survtime_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#'
#' # Define groupings for BMRKR2 levels.
#' h_survtime_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' @export
h_survtime_subgroups_df <- function(variables,
                                    data,
                                    groups_lists = list(),
                                    label_all = "All Patients") {
  checkmate::assert_character(variables$tte)
  checkmate::assert_character(variables$is_event)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)

  assert_df_with_variables(data, variables)

  checkmate::assert_string(label_all)

  # Add All Patients.
  result_all <- h_survtime_df(data[[variables$tte]], data[[variables$is_event]], data[[variables$arm]])
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  # Add Subgroups.
  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
    l_result <- lapply(l_data, function(grp) {
      result <- h_survtime_df(grp$df[[variables$tte]], grp$df[[variables$is_event]], grp$df[[variables$arm]])
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"
    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' @describeIn h_survival_duration_subgroups Helper to prepare a data frame with estimates of
#'   treatment hazard ratio.
#'
#' @param strata_data (`factor`, `data.frame`, or `NULL`)\cr required if stratified analysis is performed.
#'
#' @return
#' * `h_coxph_df()` returns a `data.frame` with columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`,
#'   `conf_level`, `pval` and `pval_label`.
#'
#' @examples
#' # Extract hazard ratio for one group.
#' h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM)
#'
#' # Extract hazard ratio for one group with stratification factor.
#' h_coxph_df(adtte_f$AVAL, adtte_f$is_event, adtte_f$ARM, strata_data = adtte_f$STRATA1)
#'
#' @export
h_coxph_df <- function(tte, is_event, arm, strata_data = NULL, control = control_coxph()) {
  checkmate::assert_numeric(tte)
  checkmate::assert_logical(is_event, len = length(tte))
  assert_valid_factor(arm, n.levels = 2, len = length(tte))

  df_tte <- data.frame(tte = tte, is_event = is_event)
  strata_vars <- NULL

  if (!is.null(strata_data)) {
    if (is.data.frame(strata_data)) {
      strata_vars <- names(strata_data)
      checkmate::assert_data_frame(strata_data, nrows = nrow(df_tte))
      assert_df_with_factors(strata_data, as.list(stats::setNames(strata_vars, strata_vars)))
    } else {
      assert_valid_factor(strata_data, len = nrow(df_tte))
      strata_vars <- "strata_data"
    }
    df_tte[strata_vars] <- strata_data
  }

  l_df <- split(df_tte, arm)

  if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
    # Hazard ratio and CI.
    result <- s_coxph_pairwise(
      df = l_df[[2]],
      .ref_group = l_df[[1]],
      .in_ref_col = FALSE,
      .var = "tte",
      is_event = "is_event",
      strata = strata_vars,
      control = control
    )

    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = unname(as.numeric(result$n_tot)),
      n_tot_events = unname(as.numeric(result$n_tot_events)),
      hr = unname(as.numeric(result$hr)),
      lcl = unname(result$hr_ci[1]),
      ucl = unname(result$hr_ci[2]),
      conf_level = control[["conf_level"]],
      pval = as.numeric(result$pvalue),
      pval_label = obj_label(result$pvalue),
      stringsAsFactors = FALSE
    )
  } else if (
    (nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) ||
      (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
  ) {
    df_tte_complete <- df_tte[stats::complete.cases(df_tte), ]
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = nrow(df_tte_complete),
      n_tot_events = sum(df_tte_complete$is_event),
      hr = NA,
      lcl = NA,
      ucl = NA,
      conf_level = control[["conf_level"]],
      pval = NA,
      pval_label = NA,
      stringsAsFactors = FALSE
    )
  } else {
    df <- data.frame(
      # Dummy column needed downstream to create a nested header.
      arm = " ",
      n_tot = 0L,
      n_tot_events = 0L,
      hr = NA,
      lcl = NA,
      ucl = NA,
      conf_level = control[["conf_level"]],
      pval = NA,
      pval_label = NA,
      stringsAsFactors = FALSE
    )
  }

  df
}

#' @describeIn h_survival_duration_subgroups Summarizes estimates of the treatment hazard ratio
#'   across subgroups in a data frame. `variables` corresponds to the names of variables found in
#'   `data`, passed as a named list and requires elements `tte`, `is_event`, `arm` and
#'   optionally `subgroups` and `strata`. `groups_lists` optionally specifies
#'   groupings for `subgroups` variables.
#'
#' @return
#' * `h_coxph_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `n_tot_events`, `hr`,
#'   `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
#'
#' @examples
#' # Extract hazard ratio for multiple groups.
#' h_coxph_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f
#' )
#'
#' # Define groupings of BMRKR2 levels.
#' h_coxph_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2")
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#'
#' # Extract hazard ratio for multiple groups with stratification factors.
#' h_coxph_subgroups_df(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     arm = "ARM",
#'     subgroups = c("SEX", "BMRKR2"),
#'     strata = c("STRATA1", "STRATA2")
#'   ),
#'   data = adtte_f
#' )
#'
#' @export
h_coxph_subgroups_df <- function(variables,
                                 data,
                                 groups_lists = list(),
                                 control = control_coxph(),
                                 label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_coxph_subgroups_df() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  checkmate::assert_character(variables$tte)
  checkmate::assert_character(variables$is_event)
  checkmate::assert_character(variables$arm)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_character(variables$strata, null.ok = TRUE)
  assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
  assert_df_with_variables(data, variables)
  checkmate::assert_string(label_all)

  # Add All Patients.
  result_all <- h_coxph_df(
    tte = data[[variables$tte]],
    is_event = data[[variables$is_event]],
    arm = data[[variables$arm]],
    strata_data = if (is.null(variables$strata)) NULL else data[variables$strata],
    control = control
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"

  # Add Subgroups.
  if (is.null(variables$subgroups)) {
    result_all
  } else {
    l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)

    l_result <- lapply(l_data, function(grp) {
      result <- h_coxph_df(
        tte = grp$df[[variables$tte]],
        is_event = grp$df[[variables$is_event]],
        arm = grp$df[[variables$arm]],
        strata_data = if (is.null(variables$strata)) NULL else grp$df[variables$strata],
        control = control
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })

    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"

    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' Split data frame by subgroups
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Split a data frame into a non-nested list of subsets.
#'
#' @inheritParams argument_convention
#' @inheritParams survival_duration_subgroups
#' @param data (`data.frame`)\cr dataset to split.
#' @param subgroups (`character`)\cr names of factor variables from `data` used to create subsets.
#'   Unused levels not present in `data` are dropped. Note that the order in this vector
#'   determines the order in the downstream table.
#'
#' @return A list with subset data (`df`) and metadata about the subset (`df_labels`).
#'
#' @details Main functionality is to prepare data for use in forest plot layouts.
#'
#' @examples
#' df <- data.frame(
#'   x = c(1:5),
#'   y = factor(c("A", "B", "A", "B", "A"), levels = c("A", "B", "C")),
#'   z = factor(c("C", "C", "D", "D", "D"), levels = c("D", "C"))
#' )
#' formatters::var_labels(df) <- paste("label for", names(df))
#'
#' h_split_by_subgroups(
#'   data = df,
#'   subgroups = c("y", "z")
#' )
#'
#' h_split_by_subgroups(
#'   data = df,
#'   subgroups = c("y", "z"),
#'   groups_lists = list(
#'     y = list("AB" = c("A", "B"), "C" = "C")
#'   )
#' )
#'
#' @export
h_split_by_subgroups <- function(data,
                                 subgroups,
                                 groups_lists = list()) {
  checkmate::assert_character(subgroups, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(groups_lists, names = "named")
  checkmate::assert_subset(names(groups_lists), subgroups)
  assert_df_with_factors(data, as.list(stats::setNames(subgroups, subgroups)))

  data_labels <- unname(formatters::var_labels(data))
  df_subgroups <- data[, subgroups, drop = FALSE]
  subgroup_labels <- formatters::var_labels(df_subgroups, fill = TRUE)

  l_labels <- Map(function(grp_i, name_i) {
    existing_levels <- levels(droplevels(grp_i))
    grp_levels <- if (name_i %in% names(groups_lists)) {
      # For this variable groupings are defined. We check which groups are contained in the data.
      group_list_i <- groups_lists[[name_i]]
      group_has_levels <- vapply(group_list_i, function(lvls) any(lvls %in% existing_levels), TRUE)
      names(which(group_has_levels))
    } else {
      existing_levels
    }
    df_labels <- data.frame(
      subgroup = grp_levels,
      var = name_i,
      var_label = unname(subgroup_labels[name_i]),
      stringsAsFactors = FALSE # Rationale is that subgroups may not be unique.
    )
  }, df_subgroups, names(df_subgroups))

  # Create a data frame with one row per subgroup.
  df_labels <- do.call(rbind, args = c(l_labels, make.row.names = FALSE))
  row_label <- paste0(df_labels$var, ".", df_labels$subgroup)
  row_split_var <- factor(row_label, levels = row_label)

  # Create a list of data subsets.
  lapply(split(df_labels, row_split_var), function(row_i) {
    which_row <- if (row_i$var %in% names(groups_lists)) {
      data[[row_i$var]] %in% groups_lists[[row_i$var]][[row_i$subgroup]]
    } else {
      data[[row_i$var]] == row_i$subgroup
    }
    df <- data[which_row, ]
    rownames(df) <- NULL
    formatters::var_labels(df) <- data_labels

    list(
      df = df,
      df_labels = data.frame(row_i, row.names = NULL)
    )
  })
}

#' Create a STEP graph
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Based on the STEP results, creates a `ggplot` graph showing the estimated HR or OR
#' along the continuous biomarker value subgroups.
#'
#' @param df (`tibble`)\cr result of [tidy.step()].
#' @param use_percentile (`flag`)\cr whether to use percentiles for the x axis or actual
#'   biomarker values.
#' @param est (named `list`)\cr `col` and `lty` settings for estimate line.
#' @param ci_ribbon (named `list` or `NULL`)\cr `fill` and `alpha` settings for the confidence interval
#'   ribbon area, or `NULL` to not plot a CI ribbon.
#' @param col (`character`)\cr color(s).
#'
#' @return A `ggplot` STEP graph.
#'
#' @seealso Custom tidy method [tidy.step()].
#'
#' @examples
#' library(survival)
#' lung$sex <- factor(lung$sex)
#'
#' # Survival example.
#' vars <- list(
#'   time = "time",
#'   event = "status",
#'   arm = "sex",
#'   biomarker = "age"
#' )
#'
#' step_matrix <- fit_survival_step(
#'   variables = vars,
#'   data = lung,
#'   control = c(control_coxph(), control_step(num_points = 10, degree = 2))
#' )
#' step_data <- broom::tidy(step_matrix)
#'
#' # Default plot.
#' g_step(step_data)
#'
#' # Add the reference 1 horizontal line.
#' library(ggplot2)
#' g_step(step_data) +
#'   ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)
#'
#' # Use actual values instead of percentiles, different color for estimate and no CI,
#' # use log scale for y axis.
#' g_step(
#'   step_data,
#'   use_percentile = FALSE,
#'   est = list(col = "blue", lty = 1),
#'   ci_ribbon = NULL
#' ) + scale_y_log10()
#'
#' # Adding another curve based on additional column.
#' step_data$extra <- exp(step_data$`Percentile Center`)
#' g_step(step_data) +
#'   ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = "green")
#'
#' # Response example.
#' vars <- list(
#'   response = "status",
#'   arm = "sex",
#'   biomarker = "age"
#' )
#'
#' step_matrix <- fit_rsp_step(
#'   variables = vars,
#'   data = lung,
#'   control = c(
#'     control_logistic(response_definition = "I(response == 2)"),
#'     control_step()
#'   )
#' )
#' step_data <- broom::tidy(step_matrix)
#' g_step(step_data)
#'
#' @export
g_step <- function(df,
                   use_percentile = "Percentile Center" %in% names(df),
                   est = list(col = "blue", lty = 1),
                   ci_ribbon = list(fill = getOption("ggplot2.discrete.colour")[1], alpha = 0.5),
                   col = getOption("ggplot2.discrete.colour")) {
  checkmate::assert_tibble(df)
  checkmate::assert_flag(use_percentile)
  checkmate::assert_character(col, null.ok = TRUE)
  checkmate::assert_list(est, names = "named")
  checkmate::assert_list(ci_ribbon, names = "named", null.ok = TRUE)

  x_var <- ifelse(use_percentile, "Percentile Center", "Interval Center")
  df$x <- df[[x_var]]
  attrs <- attributes(df)
  df$y <- df[[attrs$estimate]]

  # Set legend names. To be modified also at call level
  legend_names <- c("Estimate", "CI 95%")

  p <- ggplot2::ggplot(df, ggplot2::aes(x = .data[["x"]], y = .data[["y"]]))

  if (!is.null(col)) {
    p <- p +
      ggplot2::scale_color_manual(values = col)
  }

  if (!is.null(ci_ribbon)) {
    if (is.null(ci_ribbon$fill)) {
      ci_ribbon$fill <- "lightblue"
    }
    p <- p + ggplot2::geom_ribbon(
      ggplot2::aes(
        ymin = .data[["ci_lower"]], ymax = .data[["ci_upper"]],
        fill = legend_names[2]
      ),
      alpha = ci_ribbon$alpha
    ) +
      scale_fill_manual(
        name = "", values = c("CI 95%" = ci_ribbon$fill)
      )
  }
  suppressMessages(p <- p +
    ggplot2::geom_line(
      ggplot2::aes(y = .data[["y"]], color = legend_names[1]),
      linetype = est$lty
    ) +
    scale_colour_manual(
      name = "", values = c("Estimate" = "blue")
    ))

  p <- p + ggplot2::labs(x = attrs$biomarker, y = attrs$estimate)
  if (use_percentile) {
    p <- p + ggplot2::scale_x_continuous(labels = scales::percent)
  }
  p
}

#' Custom tidy method for STEP results
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Tidy the STEP results into a `tibble` format ready for plotting.
#'
#' @param x (`matrix`)\cr results from [fit_survival_step()].
#' @param ... not used.
#'
#' @return A `tibble` with one row per STEP subgroup. The estimates and CIs are on the HR or OR scale,
#'   respectively. Additional attributes carry metadata also used for plotting.
#'
#' @seealso [g_step()] which consumes the result from this function.
#'
#' @method tidy step
#'
#' @examples
#' library(survival)
#' lung$sex <- factor(lung$sex)
#' vars <- list(
#'   time = "time",
#'   event = "status",
#'   arm = "sex",
#'   biomarker = "age"
#' )
#' step_matrix <- fit_survival_step(
#'   variables = vars,
#'   data = lung,
#'   control = c(control_coxph(), control_step(num_points = 10, degree = 2))
#' )
#' broom::tidy(step_matrix)
#'
#' @export
tidy.step <- function(x, ...) { # nolint
  checkmate::assert_class(x, "step")
  dat <- as.data.frame(x)
  nams <- names(dat)
  is_surv <- "loghr" %in% names(dat)
  est_var <- ifelse(is_surv, "loghr", "logor")
  new_est_var <- ifelse(is_surv, "Hazard Ratio", "Odds Ratio")
  new_y_vars <- c(new_est_var, c("ci_lower", "ci_upper"))
  names(dat)[match(est_var, nams)] <- new_est_var
  dat[, new_y_vars] <- exp(dat[, new_y_vars])
  any_is_na <- any(is.na(dat[, new_y_vars]))
  any_is_very_large <- any(abs(dat[, new_y_vars]) > 1e10, na.rm = TRUE)
  if (any_is_na) {
    warning(paste(
      "Missing values in the point estimate or CI columns,",
      "this will lead to holes in the `g_step()` plot"
    ))
  }
  if (any_is_very_large) {
    warning(paste(
      "Very large absolute values in the point estimate or CI columns,",
      "consider adding `scale_y_log10()` to the `g_step()` result for plotting"
    ))
  }
  if (any_is_na || any_is_very_large) {
    warning("Consider using larger `bandwidth`, less `num_points` in `control_step()` settings for fitting")
  }
  structure(
    tibble::as_tibble(dat),
    estimate = new_est_var,
    biomarker = attr(x, "variables")$biomarker,
    ci = f_conf_level(attr(x, "control")$conf_level)
  )
}

#' Combine factor levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Combine specified old factor Levels in a single new level.
#'
#' @param x (`factor`)\cr factor variable.
#' @param levels (`character`)\cr level names to be combined.
#' @param new_level (`string`)\cr name of new level.
#'
#' @return A `factor` with the new levels.
#'
#' @examples
#' x <- factor(letters[1:5], levels = letters[5:1])
#' combine_levels(x, levels = c("a", "b"))
#'
#' combine_levels(x, c("e", "b"))
#'
#' @export
combine_levels <- function(x, levels, new_level = paste(levels, collapse = "/")) {
  checkmate::assert_factor(x)
  checkmate::assert_subset(levels, levels(x))

  lvls <- levels(x)

  lvls[lvls %in% levels] <- new_level

  levels(x) <- lvls

  x
}

#' Conversion of a vector to a factor
#'
#' Converts `x` to a factor and keeps its attributes. Warns appropriately such that the user
#' can decide whether they prefer converting to factor manually (e.g. for full control of
#' factor levels).
#'
#' @param x (`vector`)\cr object to convert.
#' @param x_name (`string`)\cr name of `x`.
#' @param na_level (`string`)\cr the explicit missing level which should be used when converting a character vector.
#' @param verbose (`flag`)\cr defaults to `TRUE`. It prints out warnings and messages.
#'
#' @return A `factor` with same attributes (except class) as `x`. Does not modify `x` if already a `factor`.
#'
#' @keywords internal
as_factor_keep_attributes <- function(x,
                                      x_name = deparse(substitute(x)),
                                      na_level = "<Missing>",
                                      verbose = TRUE) {
  checkmate::assert_atomic(x)
  checkmate::assert_string(x_name)
  checkmate::assert_string(na_level)
  checkmate::assert_flag(verbose)
  if (is.factor(x)) {
    return(x)
  }
  x_class <- class(x)[1]
  if (verbose) {
    warning(paste(
      "automatically converting", x_class, "variable", x_name,
      "to factor, better manually convert to factor to avoid failures"
    ))
  }
  if (identical(length(x), 0L)) {
    warning(paste(
      x_name, "has length 0, this can lead to tabulation failures, better convert to factor"
    ))
  }
  if (is.character(x)) {
    x_no_na <- explicit_na(sas_na(x), label = na_level)
    if (any(na_level %in% x_no_na)) {
      do.call(
        structure,
        c(
          list(.Data = forcats::fct_relevel(x_no_na, na_level, after = Inf)),
          attributes(x)
        )
      )
    } else {
      do.call(structure, c(list(.Data = as.factor(x)), attributes(x)))
    }
  } else {
    do.call(structure, c(list(.Data = as.factor(x)), attributes(x)))
  }
}

#' Labels for bins in percent
#'
#' This creates labels for quantile based bins in percent. This assumes the right-closed
#' intervals as produced by [cut_quantile_bins()].
#'
#' @param probs (`numeric`)\cr the probabilities identifying the quantiles.
#'   This is a sorted vector of unique `proportion` values, i.e. between 0 and 1, where
#'   the boundaries 0 and 1 must not be included.
#' @param digits (`integer(1)`)\cr number of decimal places to round the percent numbers.
#'
#' @return A `character` vector with labels in the format `[0%,20%]`, `(20%,50%]`, etc.
#'
#' @keywords internal
bins_percent_labels <- function(probs,
                                digits = 0) {
  if (isFALSE(0 %in% probs)) probs <- c(0, probs)
  if (isFALSE(1 %in% probs)) probs <- c(probs, 1)
  checkmate::assert_numeric(probs, lower = 0, upper = 1, unique = TRUE, sorted = TRUE)
  percent <- round(probs * 100, digits = digits)
  left <- paste0(utils::head(percent, -1), "%")
  right <- paste0(utils::tail(percent, -1), "%")
  without_left_bracket <- paste0(left, ",", right, "]")
  with_left_bracket <- paste0("[", utils::head(without_left_bracket, 1))
  if (length(without_left_bracket) > 1) {
    with_left_bracket <- c(
      with_left_bracket,
      paste0("(", utils::tail(without_left_bracket, -1))
    )
  }
  with_left_bracket
}

#' Cut numeric vector into empirical quantile bins
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This cuts a numeric vector into sample quantile bins.
#'
#' @inheritParams bins_percent_labels
#' @param x (`numeric`)\cr the continuous variable values which should be cut into
#'   quantile bins. This may contain `NA` values, which are then
#'   not used for the quantile calculations, but included in the return vector.
#' @param labels (`character`)\cr the unique labels for the quantile bins. When there are `n`
#'   probabilities in `probs`, then this must be `n + 1` long.
#' @param type (`integer(1)`)\cr type of quantiles to use, see [stats::quantile()] for details.
#' @param ordered (`flag`)\cr should the result be an ordered factor.
#'
#' @return A `factor` variable with appropriately-labeled bins as levels.
#'
#' @note Intervals are closed on the right side. That is, the first bin is the interval
#'   `[-Inf, q1]` where `q1` is the first quantile, the second bin is then `(q1, q2]`, etc.,
#'   and the last bin is `(qn, +Inf]` where `qn` is the last quantile.
#'
#' @examples
#' # Default is to cut into quartile bins.
#' cut_quantile_bins(cars$speed)
#'
#' # Use custom quantiles.
#' cut_quantile_bins(cars$speed, probs = c(0.1, 0.2, 0.6, 0.88))
#'
#' # Use custom labels.
#' cut_quantile_bins(cars$speed, labels = paste0("Q", 1:4))
#'
#' # NAs are preserved in result factor.
#' ozone_binned <- cut_quantile_bins(airquality$Ozone)
#' which(is.na(ozone_binned))
#' # So you might want to make these explicit.
#' explicit_na(ozone_binned)
#'
#' @export
cut_quantile_bins <- function(x,
                              probs = c(0.25, 0.5, 0.75),
                              labels = NULL,
                              type = 7,
                              ordered = TRUE) {
  checkmate::assert_flag(ordered)
  checkmate::assert_numeric(x)
  if (isFALSE(0 %in% probs)) probs <- c(0, probs)
  if (isFALSE(1 %in% probs)) probs <- c(probs, 1)
  checkmate::assert_numeric(probs, lower = 0, upper = 1, unique = TRUE, sorted = TRUE)
  if (is.null(labels)) labels <- bins_percent_labels(probs)
  checkmate::assert_character(labels, len = length(probs) - 1, any.missing = FALSE, unique = TRUE)

  if (all(is.na(x))) {
    # Early return if there are only NAs in input.
    return(factor(x, ordered = ordered, levels = labels))
  }

  quantiles <- stats::quantile(
    x,
    probs = probs,
    type = type,
    na.rm = TRUE
  )

  checkmate::assert_numeric(quantiles, unique = TRUE)

  cut(
    x,
    breaks = quantiles,
    labels = labels,
    ordered_result = ordered,
    include.lowest = TRUE,
    right = TRUE
  )
}

#' Discard specified levels of a factor
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This discards the observations as well as the levels specified from a factor.
#'
#' @param x (`factor`)\cr the original factor.
#' @param discard (`character`)\cr levels to discard.
#'
#' @return A modified `factor` with observations as well as levels from `discard` dropped.
#'
#' @examples
#' fct_discard(factor(c("a", "b", "c")), "c")
#'
#' @export
fct_discard <- function(x, discard) {
  checkmate::assert_factor(x)
  checkmate::assert_character(discard, any.missing = FALSE)
  new_obs <- x[!(x %in% discard)]
  new_levels <- setdiff(levels(x), discard)
  factor(new_obs, levels = new_levels)
}

#' Insertion of explicit missing values in a factor
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This inserts explicit missing values in a factor based on a condition. Additionally,
#' existing `NA` values will be explicitly converted to given `na_level`.
#'
#' @param x (`factor`)\cr the original factor.
#' @param condition (`logical`)\cr positions at which to insert missing values.
#' @param na_level (`string`)\cr which level to use for missing values.
#'
#' @return A modified `factor` with inserted and existing `NA` converted to `na_level`.
#'
#' @seealso [forcats::fct_na_value_to_level()] which is used internally.
#'
#' @examples
#' fct_explicit_na_if(factor(c("a", "b", NA)), c(TRUE, FALSE, FALSE))
#'
#' @export
fct_explicit_na_if <- function(x, condition, na_level = "<Missing>") {
  checkmate::assert_factor(x, len = length(condition))
  checkmate::assert_logical(condition)
  x[condition] <- NA
  x <- forcats::fct_na_value_to_level(x, level = na_level)
  forcats::fct_drop(x, only = na_level)
}

#' Collapse factor levels and keep only those new group levels
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This collapses levels and only keeps those new group levels, in the order provided.
#' The returned factor has levels in the order given, with the possible missing level last (this will
#' only be included if there are missing values).
#'
#' @param .f (`factor` or `character`)\cr original vector.
#' @param ... (named `character`)\cr levels in each vector provided will be collapsed into
#'   the new level given by the respective name.
#' @param .na_level (`string`)\cr which level to use for other levels, which should be missing in the
#'   new factor. Note that this level must not be contained in the new levels specified in `...`.
#'
#' @return A modified `factor` with collapsed levels. Values and levels which are not included
#'   in the given `character` vector input will be set to the missing level `.na_level`.
#'
#' @note Any existing `NA`s in the input vector will not be replaced by the missing level. If needed,
#'   [explicit_na()] can be called separately on the result.
#'
#' @seealso [forcats::fct_collapse()], [forcats::fct_relevel()] which are used internally.
#'
#' @examples
#' fct_collapse_only(factor(c("a", "b", "c", "d")), TRT = "b", CTRL = c("c", "d"))
#'
#' @export
fct_collapse_only <- function(.f, ..., .na_level = "<Missing>") {
  new_lvls <- names(list(...))
  if (checkmate::test_subset(.na_level, new_lvls)) {
    stop(paste0(".na_level currently set to '", .na_level, "' must not be contained in the new levels"))
  }
  x <- forcats::fct_collapse(.f, ..., other_level = .na_level)
  do.call(forcats::fct_relevel, args = c(list(.f = x), as.list(new_lvls)))
}

#' Ungroup non-numeric statistics
#'
#' Ungroups grouped non-numeric statistics within input vectors `.formats`, `.labels`, and `.indent_mods`.
#'
#' @inheritParams argument_convention
#' @param stat_out  (named `list` of `numeric`)\cr list of numeric statistics containing the statistics to ungroup.
#'
#' @return A `list` with modified elements `stat_out`, `.formats`, `.labels`, `.levels`, and `.indent_mods`.
#'
#' @seealso [a_summary()] which uses this function internally.
#'
#' @keywords internal
ungroup_stats <- function(stat_out,
                          .formats,
                          .indent_mods) {
  checkmate::assert_list(stat_out)
  empty_pval <- "pval" %in% names(stat_out) && length(stat_out[["pval"]]) == 0
  empty_pval_counts <- "pval_counts" %in% names(stat_out) && length(stat_out[["pval_counts"]]) == 0
  stat_out <- unlist(stat_out, recursive = FALSE)

  # If p-value is empty it is removed by unlist and needs to be re-added
  if (empty_pval) stat_out[["pval"]] <- character()
  if (empty_pval_counts) stat_out[["pval_counts"]] <- character()
  .stats <- sapply(regmatches(names(stat_out), regexpr("\\.", names(stat_out)), invert = TRUE), function(xi) xi[[1]])

  # Ungroup stats
  .formats <- lapply(.stats, function(x) {
    .formats[[if (!grepl("\\.", x)) x else regmatches(x, regexpr("\\.", x), invert = TRUE)[[1]][1]]]
  })

  .indent_mods <- sapply(.stats, function(x) {
    .indent_mods[[if (!grepl("\\.", x)) x else regmatches(x, regexpr("\\.", x), invert = TRUE)[[1]][1]]]
  })

  list(
    x = stat_out,
    .formats = .formats,
    .indent_mods = .indent_mods
  )
}

#' Helper function to create a new SMQ variable in ADAE by stacking SMQ and/or CQ records.
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to create a new SMQ variable in ADAE that consists of all adverse events belonging to
#' selected Standardized/Customized queries. The new dataset will only contain records of the adverse events
#' belonging to any of the selected baskets. Remember that `na_str` must match the needed pre-processing
#' done with [df_explicit_na()] to have the desired output.
#'
#' @inheritParams argument_convention
#' @param baskets (`character`)\cr variable names of the selected Standardized/Customized queries.
#' @param smq_varlabel (`string`)\cr a label for the new variable created.
#' @param keys (`character`)\cr names of the key variables to be returned along with the new variable created.
#' @param aag_summary (`data.frame`)\cr containing the SMQ baskets and the levels of interest for the final SMQ
#'   variable. This is useful when there are some levels of interest that are not observed in the `df` dataset.
#'   The two columns of this dataset should be named `basket` and `basket_name`.
#'
#' @return A `data.frame` with variables in `keys` taken from `df` and new variable SMQ containing
#'   records belonging to the baskets selected via the `baskets` argument.
#'
#' @examples
#' adae <- tern_ex_adae[1:20, ] %>% df_explicit_na()
#' h_stack_by_baskets(df = adae)
#'
#' aag <- data.frame(
#'   NAMVAR = c("CQ01NAM", "CQ02NAM", "SMQ01NAM", "SMQ02NAM"),
#'   REFNAME = c(
#'     "D.2.1.5.3/A.1.1.1.1 aesi", "X.9.9.9.9/Y.8.8.8.8 aesi",
#'     "C.1.1.1.3/B.2.2.3.1 aesi", "C.1.1.1.3/B.3.3.3.3 aesi"
#'   ),
#'   SCOPE = c("", "", "BROAD", "BROAD"),
#'   stringsAsFactors = FALSE
#' )
#'
#' basket_name <- character(nrow(aag))
#' cq_pos <- grep("^(CQ).+NAM$", aag$NAMVAR)
#' smq_pos <- grep("^(SMQ).+NAM$", aag$NAMVAR)
#' basket_name[cq_pos] <- aag$REFNAME[cq_pos]
#' basket_name[smq_pos] <- paste0(
#'   aag$REFNAME[smq_pos], "(", aag$SCOPE[smq_pos], ")"
#' )
#'
#' aag_summary <- data.frame(
#'   basket = aag$NAMVAR,
#'   basket_name = basket_name,
#'   stringsAsFactors = TRUE
#' )
#'
#' result <- h_stack_by_baskets(df = adae, aag_summary = aag_summary)
#' all(levels(aag_summary$basket_name) %in% levels(result$SMQ))
#'
#' h_stack_by_baskets(
#'   df = adae,
#'   aag_summary = NULL,
#'   keys = c("STUDYID", "USUBJID", "AEDECOD", "ARM"),
#'   baskets = "SMQ01NAM"
#' )
#'
#' @export
h_stack_by_baskets <- function(df,
                               baskets = grep("^(SMQ|CQ).+NAM$", names(df), value = TRUE),
                               smq_varlabel = "Standardized MedDRA Query",
                               keys = c("STUDYID", "USUBJID", "ASTDTM", "AEDECOD", "AESEQ"),
                               aag_summary = NULL,
                               na_str = "<Missing>") {
  smq_nam <- baskets[startsWith(baskets, "SMQ")]
  # SC corresponding to NAM
  smq_sc <- gsub(pattern = "NAM", replacement = "SC", x = smq_nam, fixed = TRUE)
  smq <- stats::setNames(smq_sc, smq_nam)

  checkmate::assert_character(baskets)
  checkmate::assert_string(smq_varlabel)
  checkmate::assert_data_frame(df)
  checkmate::assert_true(all(startsWith(baskets, "SMQ") | startsWith(baskets, "CQ")))
  checkmate::assert_true(all(endsWith(baskets, "NAM")))
  checkmate::assert_subset(baskets, names(df))
  checkmate::assert_subset(keys, names(df))
  checkmate::assert_subset(smq_sc, names(df))
  checkmate::assert_string(na_str)

  if (!is.null(aag_summary)) {
    assert_df_with_variables(
      df = aag_summary,
      variables = list(val = c("basket", "basket_name"))
    )
    # Warning in case there is no match between `aag_summary$basket` and `baskets` argument.
    # Honestly, I think those should completely match. Target baskets should be the same.
    if (length(intersect(baskets, unique(aag_summary$basket))) == 0) {
      warning("There are 0 baskets in common between aag_summary$basket and `baskets` argument.")
    }
  }

  var_labels <- c(formatters::var_labels(df[, keys]), "SMQ" = smq_varlabel)

  # convert `na_str` records from baskets to NA for the later loop and from wide to long steps
  df[, c(baskets, smq_sc)][df[, c(baskets, smq_sc)] == na_str] <- NA

  if (all(is.na(df[, baskets]))) { # in case there is no level for the target baskets
    df_long <- df[-seq_len(nrow(df)), keys] # we just need an empty data frame keeping all factor levels
  } else {
    # Concatenate SMQxxxNAM with corresponding SMQxxxSC
    df_cnct <- df[, c(keys, baskets[startsWith(baskets, "CQ")])]

    for (nam in names(smq)) {
      sc <- smq[nam] # SMQxxxSC corresponding to SMQxxxNAM
      nam_notna <- !is.na(df[[nam]])
      new_colname <- paste(nam, sc, sep = "_")
      df_cnct[nam_notna, new_colname] <- paste0(df[[nam]], "(", df[[sc]], ")")[nam_notna]
    }

    df_cnct$unique_id <- seq(1, nrow(df_cnct))
    var_cols <- names(df_cnct)[!(names(df_cnct) %in% c(keys, "unique_id"))]
    # have to convert df_cnct from tibble to data frame
    # as it throws a warning otherwise about rownames.
    # tibble do not support rownames and reshape creates rownames

    df_long <- stats::reshape(
      data = as.data.frame(df_cnct),
      varying = var_cols,
      v.names = "SMQ",
      idvar = names(df_cnct)[names(df_cnct) %in% c(keys, "unique_id")],
      direction = "long",
      new.row.names = seq(prod(length(var_cols), nrow(df_cnct)))
    )

    df_long <- df_long[!is.na(df_long[, "SMQ"]), !(names(df_long) %in% c("time", "unique_id"))]
    df_long$SMQ <- as.factor(df_long$SMQ)
  }

  smq_levels <- setdiff(levels(df_long[["SMQ"]]), na_str)

  if (!is.null(aag_summary)) {
    # A warning in case there is no match between df and aag_summary records
    if (length(intersect(smq_levels, unique(aag_summary$basket_name))) == 0) {
      warning("There are 0 basket levels in common between aag_summary$basket_name and df.")
    }
    df_long[["SMQ"]] <- factor(
      df_long[["SMQ"]],
      levels = sort(
        c(
          smq_levels,
          setdiff(unique(aag_summary$basket_name), smq_levels)
        )
      )
    )
  } else {
    all_na_basket_flag <- vapply(df[, baskets], function(x) {
      all(is.na(x))
    }, FUN.VALUE = logical(1))
    all_na_basket <- baskets[all_na_basket_flag]

    df_long[["SMQ"]] <- factor(
      df_long[["SMQ"]],
      levels = sort(c(smq_levels, all_na_basket))
    )
  }
  formatters::var_labels(df_long) <- var_labels
  tibble::tibble(df_long)
}

#' Get default statistical methods and their associated formats, labels, and indent modifiers
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Utility functions to get valid statistic methods for different method groups
#' (`.stats`) and their associated formats (`.formats`), labels (`.labels`), and indent modifiers
#' (`.indent_mods`). This utility is used across `tern`, but some of its working principles can be
#' seen in [analyze_vars()]. See notes to understand why this is experimental.
#'
#' @param stats (`character`)\cr statistical methods to return defaults for.
#' @param levels_per_stats (named `list` of `character` or `NULL`)\cr named list where the name of each element is a
#'   statistic from `stats` and each element is the levels of a `factor` or `character` variable (or variable name),
#'   each corresponding to a single row, for which the named statistic should be calculated for. If a statistic is only
#'   calculated once (one row), the element can be either `NULL` or the name of the statistic. Each list element will be
#'   flattened such that the names of the list elements returned by the function have the format `statistic.level` (or
#'   just `statistic` for statistics calculated for a single row). Defaults to `NULL`.
#' @param tern_defaults (`list` or `vector`)\cr defaults to use to fill in missing values if no user input is given.
#'   Must be of the same type as the values that are being filled in (e.g. indentation must be integers).
#'
#' @details
#' Current choices for `type` are `counts` and `numeric` for [analyze_vars()] and affect `get_stats()`.
#'
#' @note
#' These defaults are experimental because we use the names of functions to retrieve the default
#' statistics. This should be generalized in groups of methods according to more reasonable groupings.
#'
#' @name default_stats_formats_labels
NULL

#' @describeIn default_stats_formats_labels Get statistics available for a given method
#'   group (analyze function). To check available defaults see `tern::tern_default_stats` list.
#'
#' @param method_groups (`character`)\cr indicates the statistical method group (`tern` analyze function)
#'   to retrieve default statistics for. A character vector can be used to specify more than one statistical
#'   method group.
#' @param stats_in (`character`)\cr statistics to retrieve for the selected method group.
#' @param add_pval (`flag`)\cr should `"pval"` (or `"pval_counts"` if `method_groups` contains
#'   `"analyze_vars_counts"`) be added to the statistical methods?
#'
#' @return
#' * `get_stats()` returns a `character` vector of statistical methods.
#'
#' @examples
#' # analyze_vars is numeric
#' num_stats <- get_stats("analyze_vars_numeric") # also the default
#'
#' # Other type
#' cnt_stats <- get_stats("analyze_vars_counts")
#'
#' # Weirdly taking the pval from count_occurrences
#' only_pval <- get_stats("count_occurrences", add_pval = TRUE, stats_in = "pval")
#'
#' # All count_occurrences
#' all_cnt_occ <- get_stats("count_occurrences")
#'
#' # Multiple
#' get_stats(c("count_occurrences", "analyze_vars_counts"))
#'
#' @export
get_stats <- function(method_groups = "analyze_vars_numeric", stats_in = NULL, add_pval = FALSE) {
  checkmate::assert_character(method_groups)
  checkmate::assert_character(stats_in, null.ok = TRUE)
  checkmate::assert_flag(add_pval)

  # Default is still numeric
  if (any(method_groups == "analyze_vars")) {
    method_groups[method_groups == "analyze_vars"] <- "analyze_vars_numeric"
  }

  type_tmp <- ifelse(any(grepl("counts$", method_groups)), "counts", "numeric") # for pval checks

  # Defaults for loop
  out <- NULL

  # Loop for multiple method groups
  for (mgi in method_groups) {
    if (mgi %in% names(tern_default_stats)) {
      out_tmp <- tern_default_stats[[mgi]]
    } else {
      stop("The selected method group (", mgi, ") has no default statistical method.")
    }
    out <- unique(c(out, out_tmp))
  }

  # If you added pval to the stats_in you certainly want it
  if (!is.null(stats_in) && any(grepl("^pval", stats_in))) {
    stats_in_pval_value <- stats_in[grepl("^pval", stats_in)]

    # Must be only one value between choices
    checkmate::assert_choice(stats_in_pval_value, c("pval", "pval_counts"))

    # Mismatch with counts and numeric
    if (any(grepl("counts", method_groups)) && stats_in_pval_value != "pval_counts" ||
      any(grepl("numeric", method_groups)) && stats_in_pval_value != "pval") { # nolint
      stop(
        "Inserted p-value (", stats_in_pval_value, ") is not valid for type ",
        type_tmp, ". Use ", paste(ifelse(stats_in_pval_value == "pval", "pval_counts", "pval")),
        " instead."
      )
    }

    # Lets add it even if present (thanks to unique)
    add_pval <- TRUE
  }

  # Mainly used in "analyze_vars" but it could be necessary elsewhere
  if (isTRUE(add_pval)) {
    if (any(grepl("counts", method_groups))) {
      out <- unique(c(out, "pval_counts"))
    } else {
      out <- unique(c(out, "pval"))
    }
  }

  # Filtering for stats_in (character vector)
  if (!is.null(stats_in)) {
    out <- intersect(stats_in, out) # It orders them too
  }

  # If intersect did not find matches (and no pval?) -> error
  if (length(out) == 0) {
    stop(
      "The selected method group(s) (", paste0(method_groups, collapse = ", "), ")",
      " do not have the required default statistical methods:\n",
      paste0(stats_in, collapse = " ")
    )
  }

  out
}

#' @describeIn default_stats_formats_labels Get statistical *names* available for a given method
#'   group (analyze function). Please use the `s_*` functions to get the statistical names.
#' @param stat_results (`list`)\cr list of statistical results. It should be used close to the end of
#'   a statistical function. See examples for a structure with two statistical results and two groups.
#' @param stat_names_in (`character`)\cr custom modification of statistical values.
#'
#' @return
#' * `get_stat_names()` returns a named list of`character` vectors, indicating the names of
#'    statistical outputs.
#'
#' @examples
#' stat_results <- list(
#'   "n" = list("M" = 1, "F" = 2),
#'   "count_fraction" = list("M" = c(1, 0.2), "F" = c(2, 0.1))
#' )
#' get_stat_names(stat_results)
#' get_stat_names(stat_results, list("n" = "argh"))
#'
#' @export
get_stat_names <- function(stat_results, stat_names_in = NULL) {
  checkmate::assert_character(names(stat_results), min.len = 1)
  checkmate::assert_list(stat_names_in, null.ok = TRUE)

  stat_nms_from_stats <- lapply(stat_results, function(si) {
    nm <- names(si)
    if (is.null(nm)) {
      nm <- rep(NA_character_, length(si)) # no statistical names
    }
    return(nm)
  })

  # Modify some with custom stat names
  if (!is.null(stat_names_in)) {
    # Stats is the main
    common_names <- intersect(names(stat_nms_from_stats), names(stat_names_in))
    stat_nms_from_stats[common_names] <- stat_names_in[common_names]
  }

  stat_nms_from_stats
}

# Utility function used to separate custom stats (user-defined functions) from defaults
.split_std_from_custom_stats <- function(stats_in) {
  out <- list(default_stats = NULL, custom_stats = NULL)
  if (is.list(stats_in)) {
    is_custom_fnc <- sapply(stats_in, is.function)
    checkmate::assert_list(stats_in[is_custom_fnc], types = "function", names = "named")
    out[["custom_stats"]] <- stats_in[is_custom_fnc]
    out[["default_stats"]] <- unlist(stats_in[!is_custom_fnc])
  } else {
    out[["default_stats"]] <- stats_in
  }

  out
}

# Utility function to apply statistical functions
.apply_stat_functions <- function(default_stat_fnc, custom_stat_fnc_list, args_list) {
  # Default checks
  checkmate::assert_function(default_stat_fnc)
  checkmate::assert_list(custom_stat_fnc_list, types = "function", null.ok = TRUE, names = "named")
  checkmate::assert_list(args_list)

  # Checking custom stats have same formals
  if (!is.null(custom_stat_fnc_list)) {
    fundamental_call_to_data <- names(formals(default_stat_fnc))[[1]]
    for (fnc in custom_stat_fnc_list) {
      if (!identical(names(formals(fnc))[[1]], fundamental_call_to_data)) {
        stop(
          "The first parameter of a custom statistical function needs to be the same (it can be `df` or `x`) ",
          "as the default statistical function. In this case your custom function has ", names(formals(fnc))[[1]],
          " as first parameter, while the default function has ", fundamental_call_to_data, "."
        )
      }
      if (!any(names(formals(fnc)) == "...")) {
        stop(
          "The custom statistical function needs to have `...` as a parameter to accept additional arguments. ",
          "In this case your custom function does not have `...`."
        )
      }
    }
  }

  # Merging
  stat_fnc_list <- c(default_stat_fnc, custom_stat_fnc_list)

  # Applying
  out <- unlist(lapply(stat_fnc_list, function(fnc) do.call(fnc, args = args_list)), recursive = FALSE)

  out
}

#' @describeIn default_stats_formats_labels Get formats corresponding to a list of statistics.
#'   To check available defaults see list `tern::tern_default_formats`.
#'
#' @param formats_in (named `vector`)\cr custom formats to use instead of defaults. Can be a character vector with
#'   values from [formatters::list_valid_format_labels()] or custom format functions. Defaults to `NULL` for any rows
#'   with no value is provided.
#'
#' @return
#' * `get_formats_from_stats()` returns a named list of formats as strings or functions.
#'
#' @note Formats in `tern` and `rtables` can be functions that take in the table cell value and
#'   return a string. This is well documented in `vignette("custom_appearance", package = "rtables")`.
#'
#' @examples
#' # Defaults formats
#' get_formats_from_stats(num_stats)
#' get_formats_from_stats(cnt_stats)
#' get_formats_from_stats(only_pval)
#' get_formats_from_stats(all_cnt_occ)
#'
#' # Addition of customs
#' get_formats_from_stats(all_cnt_occ, formats_in = c("fraction" = c("xx")))
#' get_formats_from_stats(all_cnt_occ, formats_in = list("fraction" = c("xx.xx", "xx")))
#'
#' @seealso [formatting_functions]
#'
#' @export
get_formats_from_stats <- function(stats,
                                   formats_in = NULL,
                                   levels_per_stats = NULL,
                                   tern_defaults = tern_default_formats) {
  checkmate::assert_character(stats, min.len = 1)
  # It may be a list if there is a function in the formats
  if (checkmate::test_list(formats_in, null.ok = TRUE)) {
    checkmate::assert_list(formats_in, null.ok = TRUE)
    # Or it may be a vector of characters
  } else {
    checkmate::assert_character(formats_in, null.ok = TRUE)
  }
  checkmate::assert_list(levels_per_stats, null.ok = TRUE)

  # If levels_per_stats not given, assume one row per statistic
  if (is.null(levels_per_stats)) levels_per_stats <- as.list(stats) %>% setNames(stats)

  # Apply custom formats
  out <- .fill_in_vals_by_stats(levels_per_stats, formats_in, tern_defaults)

  # Default to NULL if no format
  out[names(out) == out] <- list(NULL)

  out
}

#' @describeIn default_stats_formats_labels Get labels corresponding to a list of statistics.
#'   To check for available defaults see list `tern::tern_default_labels`.
#'
#' @param labels_in (named `character`)\cr custom labels to use instead of defaults. If no value is provided, the
#'   variable level (if rows correspond to levels of a variable) or statistic name will be used as label.
#'
#' @return
#' * `get_labels_from_stats()` returns a named list of labels as strings.
#'
#' @examples
#' # Defaults labels
#' get_labels_from_stats(num_stats)
#' get_labels_from_stats(cnt_stats)
#' get_labels_from_stats(only_pval)
#' get_labels_from_stats(all_cnt_occ)
#'
#' # Addition of customs
#' get_labels_from_stats(all_cnt_occ, labels_in = c("fraction" = "Fraction"))
#' get_labels_from_stats(all_cnt_occ, labels_in = list("fraction" = c("Some more fractions")))
#'
#' @export
get_labels_from_stats <- function(stats,
                                  labels_in = NULL,
                                  levels_per_stats = NULL,
                                  tern_defaults = tern_default_labels) {
  checkmate::assert_character(stats, min.len = 1)
  # It may be a list
  if (checkmate::test_list(labels_in, null.ok = TRUE)) {
    checkmate::assert_list(labels_in, null.ok = TRUE)
    # Or it may be a vector of characters
  } else {
    checkmate::assert_character(labels_in, null.ok = TRUE)
  }
  checkmate::assert_list(levels_per_stats, null.ok = TRUE)

  # If levels_per_stats not given, assume one row per statistic
  if (is.null(levels_per_stats)) levels_per_stats <- as.list(stats) %>% setNames(stats)

  # Apply custom labels
  out <- .fill_in_vals_by_stats(levels_per_stats, labels_in, tern_defaults)
  out
}

#' @describeIn default_stats_formats_labels Get row indent modifiers corresponding to a list of statistics/rows.
#'
#' @param indents_in (named `integer`)\cr custom row indent modifiers to use instead of defaults. Defaults to `0L` for
#'   all values.
#' @param row_nms `r lifecycle::badge("deprecated")` Deprecation cycle started. See the `levels_per_stats` parameter
#'   for details.
#'
#' @return
#' * `get_indents_from_stats()` returns a named list of indentation modifiers as integers.
#'
#' @examples
#' get_indents_from_stats(all_cnt_occ, indents_in = 3L)
#' get_indents_from_stats(all_cnt_occ, indents_in = list(count = 2L, count_fraction = 5L))
#' get_indents_from_stats(
#'   all_cnt_occ,
#'   indents_in = list(a = 2L, count.a = 1L, count.b = 5L)
#' )
#'
#' @export
get_indents_from_stats <- function(stats,
                                   indents_in = NULL,
                                   levels_per_stats = NULL,
                                   tern_defaults = as.list(rep(0L, length(stats))) %>% setNames(stats),
                                   row_nms = lifecycle::deprecated()) {
  checkmate::assert_character(stats, min.len = 1)
  # It may be a list
  if (checkmate::test_list(indents_in, null.ok = TRUE)) {
    checkmate::assert_list(indents_in, null.ok = TRUE)
    # Or it may be a vector of integers
  } else {
    checkmate::assert_integerish(indents_in, null.ok = TRUE)
  }
  checkmate::assert_list(levels_per_stats, null.ok = TRUE)

  # If levels_per_stats not given, assume one row per statistic
  if (is.null(levels_per_stats)) levels_per_stats <- as.list(stats) %>% setNames(stats)

  # Single indentation level for all rows
  if (is.null(names(indents_in)) && length(indents_in) == 1) {
    out <- rep(indents_in, length(levels_per_stats %>% unlist()))
    return(out)
  }

  # Apply custom indentation
  out <- .fill_in_vals_by_stats(levels_per_stats, indents_in, tern_defaults)
  out
}

# Function to loop over each stat and levels to set correct values
.fill_in_vals_by_stats <- function(levels_per_stats, user_in, tern_defaults) {
  out <- list()

  for (stat_i in names(levels_per_stats)) {
    # Get all levels of the statistic
    all_lvls <- levels_per_stats[[stat_i]]

    if ((length(all_lvls) == 1 && all_lvls == stat_i) || is.null(all_lvls)) { # One row per statistic
      out[[stat_i]] <- if (stat_i %in% names(user_in)) { # 1. Check for stat_i in user input
        user_in[[stat_i]]
      } else if (stat_i %in% names(tern_defaults)) { # 2. Check for stat_i in tern defaults
        tern_defaults[[stat_i]]
      } else { # 3. Otherwise stat_i
        stat_i
      }
    } else { # One row per combination of variable level and statistic
      # Loop over levels for each statistic
      for (lev_i in all_lvls) {
        # Construct row name (stat_i.lev_i)
        row_nm <- paste(stat_i, lev_i, sep = ".")

        out[[row_nm]] <- if (row_nm %in% names(user_in)) { # 1. Check for stat_i.lev_i in user input
          user_in[[row_nm]]
        } else if (lev_i %in% names(user_in)) { # 2. Check for lev_i in user input
          user_in[[lev_i]]
        } else if (stat_i %in% names(user_in)) { # 3. Check for stat_i in user input
          user_in[[stat_i]]
        } else if (lev_i %in% names(tern_defaults)) { # 4. Check for lev_i in tern defaults (only used for labels)
          tern_defaults[[lev_i]]
        } else if (stat_i %in% names(tern_defaults)) { # 5. Check for stat_i in tern defaults
          tern_defaults[[stat_i]]
        } else { # 6. Otherwise lev_i
          lev_i
        }
      }
    }
  }

  out
}

# Custom unlist function to retain NULL as "NULL" or NA
.unlist_keep_nulls <- function(lst, null_placeholder = "NULL", recursive = FALSE) {
  lapply(lst, function(x) if (is.null(x)) null_placeholder else x) %>%
    unlist(recursive = recursive)
}

#' Update labels according to control specifications
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Given a list of statistic labels and and a list of control parameters, updates labels with a relevant
#' control specification. For example, if control has element `conf_level` set to `0.9`, the default
#' label for statistic `mean_ci` will be updated to `"Mean 90% CI"`. Any labels that are supplied
#' via `labels_custom` will not be updated regardless of `control`.
#'
#' @param labels_default (named `character`)\cr a named vector of statistic labels to modify
#'   according to the control specifications. Labels that are explicitly defined in `labels_custom` will
#'   not be affected.
#' @param labels_custom (named `character`)\cr named vector of labels that are customized by
#'   the user and should not be affected by `control`.
#' @param control (named `list`)\cr list of control parameters to apply to adjust default labels.
#'
#' @return A named character vector of labels with control specifications applied to relevant labels.
#'
#' @examples
#' control <- list(conf_level = 0.80, quantiles = c(0.1, 0.83), test_mean = 0.57)
#' get_labels_from_stats(c("mean_ci", "quantiles", "mean_pval")) %>%
#'   labels_use_control(control = control)
#'
#' @export
labels_use_control <- function(labels_default, control, labels_custom = NULL) {
  if ("conf_level" %in% names(control)) {
    labels_default <- sapply(
      names(labels_default),
      function(x) {
        if (!x %in% names(labels_custom)) {
          gsub(labels_default[[x]], pattern = "[0-9]+% CI", replacement = f_conf_level(control[["conf_level"]]))
        } else {
          labels_default[[x]]
        }
      }
    )
  }
  if ("quantiles" %in% names(control) && "quantiles" %in% names(labels_default) &&
    !"quantiles" %in% names(labels_custom)) { # nolint
    labels_default["quantiles"] <- gsub(
      "[0-9]+% and [0-9]+", paste0(control[["quantiles"]][1] * 100, "% and ", control[["quantiles"]][2] * 100, ""),
      labels_default["quantiles"]
    )
  }
  if ("quantiles" %in% names(control) && "quantiles_lower" %in% names(labels_default) &&
    !"quantiles_lower" %in% names(labels_custom)) { # nolint
    labels_default["quantiles_lower"] <- gsub(
      "[0-9]+%-ile", paste0(control[["quantiles"]][1] * 100, "%-ile", ""),
      labels_default["quantiles_lower"]
    )
  }
  if ("quantiles" %in% names(control) && "quantiles_upper" %in% names(labels_default) &&
    !"quantiles_upper" %in% names(labels_custom)) { # nolint
    labels_default["quantiles_upper"] <- gsub(
      "[0-9]+%-ile", paste0(control[["quantiles"]][2] * 100, "%-ile", ""),
      labels_default["quantiles_upper"]
    )
  }
  if ("test_mean" %in% names(control) && "mean_pval" %in% names(labels_default) &&
    !"mean_pval" %in% names(labels_custom)) { # nolint
    labels_default["mean_pval"] <- gsub(
      "p-value \\(H0: mean = [0-9\\.]+\\)", f_pval(control[["test_mean"]]), labels_default["mean_pval"]
    )
  }

  labels_default
}

# tern_default_stats -----------------------------------------------------------
#' @describeIn default_stats_formats_labels Named list of available statistics by method group for `tern`.
#'
#' @format
#' * `tern_default_stats` is a named list of available statistics, with each element
#'   named for their corresponding statistical method group.
#'
#' @export
tern_default_stats <- list(
  abnormal = c("fraction"),
  abnormal_by_baseline = c("fraction"),
  abnormal_by_marked = c("count_fraction", "count_fraction_fixed_dp"),
  abnormal_by_worst_grade = c("count_fraction", "count_fraction_fixed_dp"),
  abnormal_by_worst_grade_worsen = c("fraction"),
  analyze_patients_exposure_in_cols = c("n_patients", "sum_exposure"),
  analyze_vars_counts = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "fraction", "n_blq"),
  analyze_vars_numeric = c(
    "n", "sum", "mean", "sd", "se", "mean_sd", "mean_se", "mean_ci", "mean_sei", "mean_sdi", "mean_pval",
    "median", "mad", "median_ci", "quantiles", "iqr", "range", "min", "max", "median_range", "cv",
    "geom_mean", "geom_mean_ci", "geom_cv",
    "median_ci_3d",
    "mean_ci_3d", "geom_mean_ci_3d"
  ),
  count_cumulative = c("count_fraction", "count_fraction_fixed_dp"),
  count_missed_doses = c("n", "count_fraction", "count_fraction_fixed_dp"),
  count_occurrences = c("count", "count_fraction", "count_fraction_fixed_dp", "fraction"),
  count_occurrences_by_grade = c("count_fraction", "count_fraction_fixed_dp"),
  count_patients_with_event = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  count_patients_with_flags = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  count_values = c("n", "count", "count_fraction", "count_fraction_fixed_dp", "n_blq"),
  coxph_pairwise = c("pvalue", "hr", "hr_ci", "n_tot", "n_tot_events"),
  estimate_incidence_rate = c("person_years", "n_events", "rate", "rate_ci", "n_unique", "n_rate"),
  estimate_multinomial_response = c("n_prop", "prop_ci"),
  estimate_odds_ratio = c("or_ci", "n_tot"),
  estimate_proportion = c("n_prop", "prop_ci"),
  estimate_proportion_diff = c("diff", "diff_ci"),
  summarize_ancova = c("n", "lsmean", "lsmean_diff", "lsmean_diff_ci", "pval"),
  summarize_coxreg = c("n", "hr", "ci", "pval", "pval_inter"),
  summarize_glm_count = c("n", "rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
  summarize_num_patients = c("unique", "nonunique", "unique_count"),
  summarize_patients_events_in_cols = c("unique", "all"),
  surv_time = c(
    "median", "median_ci", "median_ci_3d", "quantiles",
    "quantiles_lower", "quantiles_upper", "range_censor", "range_event", "range"
  ),
  surv_timepoint = c(
    "pt_at_risk", "event_free_rate", "rate_se", "rate_ci", "rate_diff", "rate_diff_ci", "ztest_pval",
    "event_free_rate_3d"
  ),
  tabulate_rsp_biomarkers = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
  tabulate_rsp_subgroups = c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval"),
  tabulate_survival_biomarkers = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
  tabulate_survival_subgroups = c("n_tot_events", "n_events", "n_tot", "n", "median", "hr", "ci", "pval"),
  test_proportion_diff = c("pval")
)

# tern_default_formats ---------------------------------------------------------
#' @describeIn default_stats_formats_labels Named vector of default formats for `tern`.
#'
#' @format
#' * `tern_default_formats` is a named vector of available default formats, with each element
#'   named for their corresponding statistic.
#'
#' @export
tern_default_formats <- c(
  fraction = format_fraction_fixed_dp,
  unique = format_count_fraction_fixed_dp,
  nonunique = "xx",
  unique_count = "xx",
  n = "xx.",
  count = "xx.",
  count_fraction = format_count_fraction,
  count_fraction_fixed_dp = format_count_fraction_fixed_dp,
  n_blq = "xx.",
  sum = "xx.x",
  mean = "xx.x",
  sd = "xx.x",
  se = "xx.x",
  mean_sd = "xx.x (xx.x)",
  mean_se = "xx.x (xx.x)",
  mean_ci = "(xx.xx, xx.xx)",
  mean_sei = "(xx.xx, xx.xx)",
  mean_sdi = "(xx.xx, xx.xx)",
  mean_pval = "x.xxxx | (<0.0001)",
  mean_ci_3d = "xx.xx (xx.xx - xx.xx)",
  median = "xx.x",
  mad = "xx.x",
  median_ci = "(xx.xx, xx.xx)",
  median_ci_3d = "xx.xx (xx.xx - xx.xx)",
  quantiles = "xx.x - xx.x",
  quantiles_lower = "xx.xx (xx.xx - xx.xx)",
  quantiles_upper = "xx.xx (xx.xx - xx.xx)",
  iqr = "xx.x",
  range = "xx.x - xx.x",
  min = "xx.x",
  max = "xx.x",
  median_range = "xx.x (xx.x - xx.x)",
  cv = "xx.x",
  geom_mean = "xx.x",
  geom_mean_ci = "(xx.xx, xx.xx)",
  geom_mean_ci_3d = "xx.xx (xx.xx - xx.xx)",
  geom_cv = "xx.x",
  pval = "x.xxxx | (<0.0001)",
  pval_counts = "x.xxxx | (<0.0001)",
  range_censor = "xx.x to xx.x",
  range_event = "xx.x to xx.x",
  rate = "xx.xxxx",
  rate_ci = "(xx.xxxx, xx.xxxx)",
  rate_ratio = "xx.xxxx",
  rate_ratio_ci = "(xx.xxxx, xx.xxxx)"
)

# tern_default_labels ----------------------------------------------------------
#' @describeIn default_stats_formats_labels Named `character` vector of default labels for `tern`.
#'
#' @format
#' * `tern_default_labels` is a named `character` vector of available default labels, with each element
#'   named for their corresponding statistic.
#'
#' @export
tern_default_labels <- c(
  unique = "Number of patients with at least one event",
  nonunique = "Number of events",
  n = "n",
  n_blq = "n_blq",
  sum = "Sum",
  mean = "Mean",
  sd = "SD",
  se = "SE",
  mean_sd = "Mean (SD)",
  mean_se = "Mean (SE)",
  mean_ci = "Mean 95% CI",
  mean_sei = "Mean -/+ 1xSE",
  mean_sdi = "Mean -/+ 1xSD",
  mean_pval = "Mean p-value (H0: mean = 0)",
  mean_ci_3d = "Mean (95% CI)",
  median = "Median",
  mad = "Median Absolute Deviation",
  median_ci = "Median 95% CI",
  median_ci_3d = "Median (95% CI)",
  quantiles = "25% and 75%-ile",
  quantiles_lower = "25%-ile (95% CI)",
  quantiles_upper = "75%-ile (95% CI)",
  iqr = "IQR",
  range = "Min - Max",
  min = "Minimum",
  max = "Maximum",
  median_range = "Median (Min - Max)",
  cv = "CV (%)",
  geom_mean = "Geometric Mean",
  geom_mean_ci = "Geometric Mean 95% CI",
  geom_mean_ci_3d = "Geometric Mean (95% CI)",
  geom_cv = "CV % Geometric Mean",
  pval = "p-value (t-test)", # Default for numeric
  pval_counts = "p-value (chi-squared test)", # Default for counts
  rate = "Adjusted Rate",
  rate_ratio = "Adjusted Rate Ratio"
)

#' @describeIn default_stats_formats_labels Quick function to retrieve default formats for summary statistics:
#'   [analyze_vars()] and [analyze_vars_in_cols()] principally.
#'
#' @param type (`string`)\cr `"numeric"` or `"counts"`.
#'
#' @return
#' * `summary_formats()` returns a named `vector` of default statistic formats for the given data type.
#'
#' @examples
#' summary_formats()
#' summary_formats(type = "counts", include_pval = TRUE)
#'
#' @export
summary_formats <- function(type = "numeric", include_pval = FALSE) {
  met_grp <- paste0(c("analyze_vars", type), collapse = "_")
  get_formats_from_stats(get_stats(met_grp, add_pval = include_pval))
}

#' @describeIn default_stats_formats_labels Quick function to retrieve default labels for summary statistics.
#'   Returns labels of descriptive statistics which are understood by `rtables`. Similar to `summary_formats`.
#'
#' @param include_pval (`flag`)\cr same as the `add_pval` argument in [get_stats()].
#'
#' @details
#' `summary_*` quick get functions for labels or formats uses `get_stats` and `get_labels_from_stats` or
#' `get_formats_from_stats` respectively to retrieve relevant information.
#'
#' @return
#' * `summary_labels` returns a named `vector` of default statistic labels for the given data type.
#'
#' @examples
#' summary_labels()
#' summary_labels(type = "counts", include_pval = TRUE)
#'
#' @export
summary_labels <- function(type = "numeric", include_pval = FALSE) {
  met_grp <- paste0(c("analyze_vars", type), collapse = "_")
  get_labels_from_stats(get_stats(met_grp, add_pval = include_pval))
}

#' Control function for subgroup treatment effect pattern (STEP) calculations
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for STEP calculations.
#'
#' @param biomarker (`numeric` or `NULL`)\cr optional provision of the numeric biomarker variable, which
#'   could be used to infer `bandwidth`, see below.
#' @param use_percentile (`flag`)\cr if `TRUE`, the running windows are created according to
#'   quantiles rather than actual values, i.e. the bandwidth refers to the percentage of data
#'   covered in each window. Suggest `TRUE` if the biomarker variable is not uniformly
#'   distributed.
#' @param bandwidth (`numeric(1)` or `NULL`)\cr indicating the bandwidth of each window.
#'   Depending on the argument `use_percentile`, it can be either the length of actual-value
#'   windows on the real biomarker scale, or percentage windows.
#'   If `use_percentile = TRUE`, it should be a number between 0 and 1.
#'   If `NULL`, treat the bandwidth to be infinity, which means only one global model will be fitted.
#'   By default, `0.25` is used for percentage windows and one quarter of the range of the `biomarker`
#'   variable for actual-value windows.
#' @param degree (`integer(1)`)\cr the degree of polynomial function of the biomarker as an interaction term
#'   with the treatment arm fitted at each window. If 0 (default), then the biomarker variable
#'   is not included in the model fitted in each biomarker window.
#' @param num_points (`integer(1)`)\cr the number of points at which the hazard ratios are estimated. The
#'   smallest number is 2.
#'
#' @return A list of components with the same names as the arguments, except `biomarker` which is
#'   just used to calculate the `bandwidth` in case that actual biomarker windows are requested.
#'
#' @examples
#' # Provide biomarker values and request actual values to be used,
#' # so that bandwidth is chosen from range.
#' control_step(biomarker = 1:10, use_percentile = FALSE)
#'
#' # Use a global model with quadratic biomarker interaction term.
#' control_step(bandwidth = NULL, degree = 2)
#'
#' # Reduce number of points to be used.
#' control_step(num_points = 10)
#'
#' @export
control_step <- function(biomarker = NULL,
                         use_percentile = TRUE,
                         bandwidth,
                         degree = 0L,
                         num_points = 39L) {
  checkmate::assert_numeric(biomarker, null.ok = TRUE)
  checkmate::assert_flag(use_percentile)
  checkmate::assert_int(num_points, lower = 2)
  checkmate::assert_count(degree)

  if (missing(bandwidth)) {
    # Infer bandwidth
    bandwidth <- if (use_percentile) {
      0.25
    } else if (!is.null(biomarker)) {
      diff(range(biomarker, na.rm = TRUE)) / 4
    } else {
      NULL
    }
  } else {
    # Check bandwidth
    if (!is.null(bandwidth)) {
      if (use_percentile) {
        assert_proportion_value(bandwidth)
      } else {
        checkmate::assert_scalar(bandwidth)
        checkmate::assert_true(bandwidth > 0)
      }
    }
  }
  list(
    use_percentile = use_percentile,
    bandwidth = bandwidth,
    degree = as.integer(degree),
    num_points = as.integer(num_points)
  )
}

#' Control function for descriptive statistics
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Sets a list of parameters for summaries of descriptive statistics. Typically used internally to specify
#' details for [s_summary()]. This function family is mainly used by [analyze_vars()].
#'
#' @inheritParams argument_convention
#' @param quantiles (`numeric(2)`)\cr vector of length two to specify the quantiles to calculate.
#' @param quantile_type (`numeric(1)`)\cr number between 1 and 9 selecting quantile algorithms to be used.
#'   Default is set to 2 as this matches the default quantile algorithm in SAS `proc univariate` set by `QNTLDEF=5`.
#'   This differs from R's default. See more about `type` in [stats::quantile()].
#' @param test_mean (`numeric(1)`)\cr number to test against the mean under the null hypothesis when calculating
#'   p-value.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @export
control_analyze_vars <- function(conf_level = 0.95,
                                 quantiles = c(0.25, 0.75),
                                 quantile_type = 2,
                                 test_mean = 0) {
  checkmate::assert_vector(quantiles, len = 2)
  checkmate::assert_int(quantile_type, lower = 1, upper = 9)
  checkmate::assert_numeric(test_mean)
  lapply(quantiles, assert_proportion_value)
  assert_proportion_value(conf_level)
  list(conf_level = conf_level, quantiles = quantiles, quantile_type = quantile_type, test_mean = test_mean)
}

# Helper function to fix numeric or counts pval if necessary
.correct_num_or_counts_pval <- function(type, .stats) {
  if (type == "numeric") {
    if (!is.null(.stats) && any(grepl("^pval", .stats))) {
      .stats[grepl("^pval", .stats)] <- "pval" # tmp fix xxx
    }
  } else {
    if (!is.null(.stats) && any(grepl("^pval", .stats))) {
      .stats[grepl("^pval", .stats)] <- "pval_counts" # tmp fix xxx
    }
  }
  .stats
}

#' Analyze variables
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [analyze_vars()] creates a layout element to summarize one or more variables, using the S3
#' generic function [s_summary()] to calculate a list of summary statistics. A list of all available statistics for
#' numeric variables can be viewed by running `get_stats("analyze_vars_numeric")` and for non-numeric variables by
#' running `get_stats("analyze_vars_counts")`. Use the `.stats` parameter to specify the statistics to include in your
#' output summary table. Use `compare_with_ref_group = TRUE` to compare the variable with reference groups.
#'
#' @details
#' **Automatic digit formatting:** The number of digits to display can be automatically determined from the analyzed
#' variable(s) (`vars`) for certain statistics by setting the statistic format to `"auto"` in `.formats`.
#' This utilizes the [format_auto()] formatting function. Note that only data for the current row & variable (for all
#' columns) will be considered (`.df_row[[.var]]`, see [`rtables::additional_fun_params`]) and not the whole dataset.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options for numeric variables are: ``r shQuote(get_stats("analyze_vars_numeric"), type = "sh")``
#'
#'   Options for non-numeric variables are: ``r shQuote(get_stats("analyze_vars_counts"), type = "sh")``
#'
#' @name analyze_variables
#' @order 1
NULL

#' @describeIn analyze_variables S3 generic function to produces a variable summary.
#'
#' @return
#' * `s_summary()` returns different statistics depending on the class of `x`.
#'
#' @export
s_summary <- function(x, ...) {
  UseMethod("s_summary", x)
}

#' @describeIn analyze_variables Method for `numeric` class.
#'
#' @param control (`list`)\cr parameters for descriptive statistics details, specified by using
#'   the helper function [control_analyze_vars()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for mean and median.
#'   * `quantiles` (`numeric(2)`)\cr vector of length two to specify the quantiles.
#'   * `quantile_type` (`numeric(1)`)\cr between 1 and 9 selecting quantile algorithms to be used.
#'     See more about `type` in [stats::quantile()].
#'   * `test_mean` (`numeric(1)`)\cr value to test against the mean under the null hypothesis when calculating p-value.
#'
#' @return
#'   * If `x` is of class `numeric`, returns a `list` with the following named `numeric` items:
#'     * `n`: The [length()] of `x`.
#'     * `sum`: The [sum()] of `x`.
#'     * `mean`: The [mean()] of `x`.
#'     * `sd`: The [stats::sd()] of `x`.
#'     * `se`: The standard error of `x` mean, i.e.: (`sd(x) / sqrt(length(x))`).
#'     * `mean_sd`: The [mean()] and [stats::sd()] of `x`.
#'     * `mean_se`: The [mean()] of `x` and its standard error (see above).
#'     * `mean_ci`: The CI for the mean of `x` (from [stat_mean_ci()]).
#'     * `mean_sei`: The SE interval for the mean of `x`, i.e.: ([mean()] -/+ [stats::sd()] / [sqrt()]).
#'     * `mean_sdi`: The SD interval for the mean of `x`, i.e.: ([mean()] -/+ [stats::sd()]).
#'     * `mean_pval`: The two-sided p-value of the mean of `x` (from [stat_mean_pval()]).
#'     * `median`: The [stats::median()] of `x`.
#'     * `mad`: The median absolute deviation of `x`, i.e.: ([stats::median()] of `xc`,
#'       where `xc` = `x` - [stats::median()]).
#'     * `median_ci`: The CI for the median of `x` (from [stat_median_ci()]).
#'     * `quantiles`: Two sample quantiles of `x` (from [stats::quantile()]).
#'     * `iqr`: The [stats::IQR()] of `x`.
#'     * `range`: The [range_noinf()] of `x`.
#'     * `min`: The [max()] of `x`.
#'     * `max`: The [min()] of `x`.
#'     * `median_range`: The [median()] and [range_noinf()] of `x`.
#'     * `cv`: The coefficient of variation of `x`, i.e.: ([stats::sd()] / [mean()] * 100).
#'     * `geom_mean`: The geometric mean of `x`, i.e.: (`exp(mean(log(x)))`).
#'     * `geom_cv`: The geometric coefficient of variation of `x`, i.e.: (`sqrt(exp(sd(log(x)) ^ 2) - 1) * 100`).
#'
#' @note
#' * If `x` is an empty vector, `NA` is returned. This is the expected feature so as to return `rcell` content in
#'   `rtables` when the intersection of a column and a row delimits an empty data selection.
#' * When the `mean` function is applied to an empty vector, `NA` will be returned instead of `NaN`, the latter
#'   being standard behavior in R.
#'
#' @method s_summary numeric
#'
#' @examples
#' # `s_summary.numeric`
#'
#' ## Basic usage: empty numeric returns NA-filled items.
#' s_summary(numeric())
#'
#' ## Management of NA values.
#' x <- c(NA_real_, 1)
#' s_summary(x, na_rm = TRUE)
#' s_summary(x, na_rm = FALSE)
#'
#' x <- c(NA_real_, 1, 2)
#' s_summary(x)
#'
#' ## Benefits in `rtables` contructions:
#' dta_test <- data.frame(
#'   Group = rep(LETTERS[seq(3)], each = 2),
#'   sub_group = rep(letters[seq(2)], each = 3),
#'   x = seq(6)
#' )
#'
#' ## The summary obtained in with `rtables`:
#' basic_table() %>%
#'   split_cols_by(var = "Group") %>%
#'   split_rows_by(var = "sub_group") %>%
#'   analyze(vars = "x", afun = s_summary) %>%
#'   build_table(df = dta_test)
#'
#' ## By comparison with `lapply`:
#' X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group)))
#' lapply(X, function(x) s_summary(x$x))
#'
#' @export
s_summary.numeric <- function(x, control = control_analyze_vars(), ...) {
  checkmate::assert_numeric(x)
  args_list <- list(...)
  .N_row <- args_list[[".N_row"]] # nolint
  .N_col <- args_list[[".N_col"]] # nolint
  na_rm <- args_list[["na_rm"]] %||% TRUE
  compare_with_ref_group <- args_list[["compare_with_ref_group"]]

  if (na_rm) {
    x <- x[!is.na(x)]
  }

  y <- list()

  y$n <- c("n" = length(x))

  y$sum <- c("sum" = ifelse(length(x) == 0, NA_real_, sum(x, na.rm = FALSE)))

  y$mean <- c("mean" = ifelse(length(x) == 0, NA_real_, mean(x, na.rm = FALSE)))

  y$sd <- c("sd" = stats::sd(x, na.rm = FALSE))

  y$se <- c("se" = stats::sd(x, na.rm = FALSE) / sqrt(length(stats::na.omit(x))))

  y$mean_sd <- c(y$mean, "sd" = stats::sd(x, na.rm = FALSE))

  y$mean_se <- c(y$mean, y$se)

  mean_ci <- stat_mean_ci(x, conf_level = control$conf_level, na.rm = FALSE, gg_helper = FALSE)
  y$mean_ci <- formatters::with_label(mean_ci, paste("Mean", f_conf_level(control$conf_level)))

  mean_sei <- y$mean[[1]] + c(-1, 1) * stats::sd(x, na.rm = FALSE) / sqrt(y$n)
  names(mean_sei) <- c("mean_sei_lwr", "mean_sei_upr")
  y$mean_sei <- formatters::with_label(mean_sei, "Mean -/+ 1xSE")

  mean_sdi <- y$mean[[1]] + c(-1, 1) * stats::sd(x, na.rm = FALSE)
  names(mean_sdi) <- c("mean_sdi_lwr", "mean_sdi_upr")
  y$mean_sdi <- formatters::with_label(mean_sdi, "Mean -/+ 1xSD")
  mean_ci_3d <- c(y$mean, y$mean_ci)
  y$mean_ci_3d <- formatters::with_label(mean_ci_3d, paste0("Mean (", f_conf_level(control$conf_level), ")"))

  mean_pval <- stat_mean_pval(x, test_mean = control$test_mean, na.rm = FALSE, n_min = 2)
  y$mean_pval <- formatters::with_label(mean_pval, paste("Mean", f_pval(control$test_mean)))

  y$median <- c("median" = stats::median(x, na.rm = FALSE))

  y$mad <- c("mad" = stats::median(x - y$median, na.rm = FALSE))

  median_ci <- stat_median_ci(x, conf_level = control$conf_level, na.rm = FALSE, gg_helper = FALSE)
  y$median_ci <- formatters::with_label(median_ci, paste("Median", f_conf_level(control$conf_level)))

  median_ci_3d <- c(y$median, median_ci)
  y$median_ci_3d <- formatters::with_label(median_ci_3d, paste0("Median (", f_conf_level(control$conf_level), ")"))

  q <- control$quantiles
  if (any(is.na(x))) {
    qnts <- rep(NA_real_, length(q))
  } else {
    qnts <- stats::quantile(x, probs = q, type = control$quantile_type, na.rm = FALSE)
  }
  names(qnts) <- paste("quantile", q, sep = "_")
  y$quantiles <- formatters::with_label(qnts, paste0(paste(paste0(q * 100, "%"), collapse = " and "), "-ile"))

  y$iqr <- c("iqr" = ifelse(
    any(is.na(x)),
    NA_real_,
    stats::IQR(x, na.rm = FALSE, type = control$quantile_type)
  ))

  y$range <- stats::setNames(range_noinf(x, na.rm = FALSE), c("min", "max"))
  y$min <- y$range[1]
  y$max <- y$range[2]

  y$median_range <- formatters::with_label(c(y$median, y$range), "Median (Min - Max)")

  y$cv <- c("cv" = unname(y$sd) / unname(y$mean) * 100)

  # Convert negative values to NA for log calculation.
  x_no_negative_vals <- x
  x_no_negative_vals[x_no_negative_vals <= 0] <- NA
  y$geom_mean <- c("geom_mean" = exp(mean(log(x_no_negative_vals), na.rm = FALSE)))
  geom_mean_ci <- stat_mean_ci(x, conf_level = control$conf_level, na.rm = FALSE, gg_helper = FALSE, geom_mean = TRUE)
  y$geom_mean_ci <- formatters::with_label(geom_mean_ci, paste("Geometric Mean", f_conf_level(control$conf_level)))

  y$geom_cv <- c("geom_cv" = sqrt(exp(stats::sd(log(x_no_negative_vals), na.rm = FALSE) ^ 2) - 1) * 100) # styler: off

  geom_mean_ci_3d <- c(y$geom_mean, y$geom_mean_ci)
  y$geom_mean_ci_3d <- formatters::with_label(
    geom_mean_ci_3d,
    paste0("Geometric Mean (", f_conf_level(control$conf_level), ")")
  )

  # Compare with reference group
  if (isTRUE(compare_with_ref_group)) {
    .ref_group <- args_list[[".ref_group"]]
    .in_ref_col <- args_list[[".in_ref_col"]]
    checkmate::assert_numeric(.ref_group)
    checkmate::assert_flag(.in_ref_col)

    y$pval <- character()
    if (!.in_ref_col && n_available(x) > 1 && n_available(.ref_group) > 1) {
      y$pval <- stats::t.test(x, .ref_group)$p.value
    }
  }

  y
}

#' @describeIn analyze_variables Method for `factor` class.
#'
#' @return
#'   * If `x` is of class `factor` or converted from `character`, returns a `list` with named `numeric` items:
#'     * `n`: The [length()] of `x`.
#'     * `count`: A list with the number of cases for each level of the factor `x`.
#'     * `count_fraction`: Similar to `count` but also includes the proportion of cases for each level of the
#'       factor `x` relative to the denominator, or `NA` if the denominator is zero.
#'
#' @note
#' * If `x` is an empty `factor`, a list is still returned for `counts` with one element
#'   per factor level. If there are no levels in `x`, the function fails.
#' * If factor variables contain `NA`, these `NA` values are excluded by default. To include `NA` values
#'   set `na_rm = FALSE` and missing values will be displayed as an `NA` level. Alternatively, an explicit
#'   factor level can be defined for `NA` values during pre-processing via [df_explicit_na()] - the
#'   default `na_level` (`"<Missing>"`) will also be excluded when `na_rm` is set to `TRUE`.
#'
#' @method s_summary factor
#'
#' @examples
#' # `s_summary.factor`
#'
#' ## Basic usage:
#' s_summary(factor(c("a", "a", "b", "c", "a")))
#'
#' # Empty factor returns zero-filled items.
#' s_summary(factor(levels = c("a", "b", "c")))
#'
#' ## Management of NA values.
#' x <- factor(c(NA, "Female"))
#' x <- explicit_na(x)
#' s_summary(x, na_rm = TRUE)
#' s_summary(x, na_rm = FALSE)
#'
#' ## Different denominators.
#' x <- factor(c("a", "a", "b", "c", "a"))
#' s_summary(x, denom = "N_row", .N_row = 10L)
#' s_summary(x, denom = "N_col", .N_col = 20L)
#'
#' @export
s_summary.factor <- function(x, denom = c("n", "N_col", "N_row"), ...) {
  assert_valid_factor(x)
  args_list <- list(...)
  .N_row <- args_list[[".N_row"]] # nolint
  .N_col <- args_list[[".N_col"]] # nolint
  na_rm <- args_list[["na_rm"]] %||% TRUE
  verbose <- args_list[["verbose"]] %||% TRUE
  compare_with_ref_group <- args_list[["compare_with_ref_group"]]

  if (na_rm) {
    x <- x[!is.na(x)] %>% fct_discard("<Missing>")
  } else {
    x <- x %>% explicit_na(label = "NA")
  }

  y <- list()

  y$n <- list("n" = c("n" = length(x))) # all list of a list

  y$count <- lapply(as.list(table(x, useNA = "ifany")), setNames, nm = "count")

  denom <- match.arg(denom) %>%
    switch(
      n = length(x),
      N_row = .N_row,
      N_col = .N_col
    )

  y$count_fraction <- lapply(
    y$count,
    function(x) {
      c(x, "p" = ifelse(denom > 0, x / denom, 0))
    }
  )

  y$count_fraction_fixed_dp <- y$count_fraction

  y$fraction <- lapply(
    y$count,
    function(count) c("num" = unname(count), "denom" = denom)
  )

  y$n_blq <- list("n_blq" = c("n_blq" = sum(grepl("BLQ|LTR|<[1-9]|<PCLLOQ", x))))


  if (isTRUE(compare_with_ref_group)) {
    .ref_group <- as_factor_keep_attributes(args_list[[".ref_group"]], verbose = verbose)
    .in_ref_col <- args_list[[".in_ref_col"]]
    checkmate::assert_flag(.in_ref_col)
    assert_valid_factor(x)
    assert_valid_factor(.ref_group)

    if (na_rm) {
      x <- x[!is.na(x)] %>% fct_discard("<Missing>")
      .ref_group <- .ref_group[!is.na(.ref_group)] %>% fct_discard("<Missing>")
    } else {
      x <- x %>% explicit_na(label = "NA")
      .ref_group <- .ref_group %>% explicit_na(label = "NA")
    }

    if ("NA" %in% levels(x)) levels(.ref_group) <- c(levels(.ref_group), "NA")
    checkmate::assert_factor(x, levels = levels(.ref_group), min.levels = 2)

    y$pval_counts <- character()
    if (!.in_ref_col && length(x) > 0 && length(.ref_group) > 0) {
      tab <- rbind(table(x), table(.ref_group))
      res <- suppressWarnings(stats::chisq.test(tab))
      y$pval_counts <- res$p.value
    }
  }

  y
}

#' @describeIn analyze_variables Method for `character` class. This makes an automatic
#'   conversion to factor (with a warning) and then forwards to the method for factors.
#'
#' @note
#' * Automatic conversion of character to factor does not guarantee that the table
#'   can be generated correctly. In particular for sparse tables this very likely can fail.
#'   It is therefore better to always pre-process the dataset such that factors are manually
#'   created from character variables before passing the dataset to [rtables::build_table()].
#'
#' @method s_summary character
#'
#' @examples
#' # `s_summary.character`
#'
#' ## Basic usage:
#' s_summary(c("a", "a", "b", "c", "a"), verbose = FALSE)
#' s_summary(c("a", "a", "b", "c", "a", ""), .var = "x", na_rm = FALSE, verbose = FALSE)
#'
#' @export
s_summary.character <- function(x, denom = c("n", "N_col", "N_row"), ...) {
  args_list <- list(...)
  na_rm <- args_list[["na_rm"]] %||% TRUE
  verbose <- args_list[["verbose"]] %||% TRUE

  if (na_rm) {
    y <- as_factor_keep_attributes(x, verbose = verbose)
  } else {
    y <- as_factor_keep_attributes(x, verbose = verbose, na_level = "NA")
  }

  s_summary(x = y, denom = denom, ...)
}

#' @describeIn analyze_variables Method for `logical` class.
#'
#' @return
#'   * If `x` is of class `logical`, returns a `list` with named `numeric` items:
#'     * `n`: The [length()] of `x` (possibly after removing `NA`s).
#'     * `count`: Count of `TRUE` in `x`.
#'     * `count_fraction`: Count and proportion of `TRUE` in `x` relative to the denominator, or `NA` if the
#'       denominator is zero. Note that `NA`s in `x` are never counted or leading to `NA` here.
#'
#' @method s_summary logical
#'
#' @examples
#' # `s_summary.logical`
#'
#' ## Basic usage:
#' s_summary(c(TRUE, FALSE, TRUE, TRUE))
#'
#' # Empty factor returns zero-filled items.
#' s_summary(as.logical(c()))
#'
#' ## Management of NA values.
#' x <- c(NA, TRUE, FALSE)
#' s_summary(x, na_rm = TRUE)
#' s_summary(x, na_rm = FALSE)
#'
#' ## Different denominators.
#' x <- c(TRUE, FALSE, TRUE, TRUE)
#' s_summary(x, denom = "N_row", .N_row = 10L)
#' s_summary(x, denom = "N_col", .N_col = 20L)
#'
#' @export
s_summary.logical <- function(x, denom = c("n", "N_col", "N_row"), ...) {
  checkmate::assert_logical(x)
  args_list <- list(...)
  .N_row <- args_list[[".N_row"]] # nolint
  .N_col <- args_list[[".N_col"]] # nolint
  na_rm <- args_list[["na_rm"]] %||% TRUE
  compare_with_ref_group <- args_list[["compare_with_ref_group"]]

  if (na_rm) {
    x <- x[!is.na(x)]
  }

  y <- list()
  y$n <- c("n" = length(x))
  denom <- match.arg(denom) %>%
    switch(
      n = length(x),
      N_row = .N_row,
      N_col = .N_col
    )
  y$count <- c("count" = sum(x, na.rm = TRUE))
  y$count_fraction <- c(y$count, "fraction" = ifelse(denom > 0, y$count / denom, 0))
  y$count_fraction_fixed_dp <- y$count_fraction
  y$fraction <- c("num" = unname(y$count), "denom" = denom)
  y$n_blq <- c("n_blq" = 0L)


  if (isTRUE(compare_with_ref_group)) {
    .ref_group <- args_list[[".ref_group"]]
    .in_ref_col <- args_list[[".in_ref_col"]]
    checkmate::assert_flag(.in_ref_col)

    if (na_rm) {
      x <- stats::na.omit(x)
      .ref_group <- stats::na.omit(.ref_group)
    } else {
      x[is.na(x)] <- FALSE
      .ref_group[is.na(.ref_group)] <- FALSE
    }

    y$pval_counts <- character()
    if (!.in_ref_col && length(x) > 0 && length(.ref_group) > 0) {
      x <- factor(x, levels = c(TRUE, FALSE))
      .ref_group <- factor(.ref_group, levels = c(TRUE, FALSE))
      tbl <- rbind(table(x), table(.ref_group))
      y$pval_counts <- suppressWarnings(prop_chisq(tbl))
    }
  }

  y
}

#' @describeIn analyze_variables Formatted analysis function which is used as `afun` in `analyze_vars()` and
#'   `compare_vars()` and as `cfun` in `summarize_colvars()`.
#'
#' @param compare_with_ref_group (`flag`)\cr whether comparison statistics should be analyzed instead of summary
#'   statistics (`compare_with_ref_group = TRUE` adds `pval` statistic comparing
#'   against reference group).
#'
#' @return
#' * `a_summary()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @note
#' * To use for comparison (with additional p-value statistic), parameter
#'   `compare_with_ref_group` must be set to `TRUE`.
#' * Ensure that either all `NA` values are converted to an explicit `NA` level or all `NA` values are left as is.
#'
#' @examples
#' a_summary(factor(c("a", "a", "b", "c", "a")), .N_row = 10, .N_col = 10)
#' a_summary(
#'   factor(c("a", "a", "b", "c", "a")),
#'   .ref_group = factor(c("a", "a", "b", "c")), compare_with_ref_group = TRUE, .in_ref_col = TRUE
#' )
#'
#' a_summary(c("A", "B", "A", "C"), .var = "x", .N_col = 10, .N_row = 10, verbose = FALSE)
#' a_summary(
#'   c("A", "B", "A", "C"),
#'   .ref_group = c("B", "A", "C"), .var = "x", compare_with_ref_group = TRUE, verbose = FALSE,
#'   .in_ref_col = FALSE
#' )
#'
#' a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10)
#' a_summary(
#'   c(TRUE, FALSE, FALSE, TRUE, TRUE),
#'   .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare_with_ref_group = TRUE,
#'   .in_ref_col = FALSE
#' )
#'
#' a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = "bla")
#' a_summary(rnorm(10, 5, 1),
#'   .ref_group = rnorm(20, -5, 1), .var = "bla", compare_with_ref_group = TRUE,
#'   .in_ref_col = FALSE
#' )
#'
#' @export
a_summary <- function(x,
                      ...,
                      .stats = NULL,
                      .stat_names = NULL,
                      .formats = NULL,
                      .labels = NULL,
                      .indent_mods = NULL) {
  dots_extra_args <- list(...)

  # Check if there are user-defined functions
  default_and_custom_stats_list <- .split_std_from_custom_stats(.stats)
  .stats <- default_and_custom_stats_list$default_stats
  custom_stat_functions <- default_and_custom_stats_list$custom_stats

  # Correction of the pval indication if it is numeric or counts
  type <- ifelse(is.numeric(x), "numeric", "counts") # counts is "categorical"
  .stats <- .correct_num_or_counts_pval(type, .stats)

  # Adding automatically extra parameters to the statistic function (see ?rtables::additional_fun_params)
  extra_afun_params <- retrieve_extra_afun_params(
    names(dots_extra_args$.additional_fun_parameters)
  )
  dots_extra_args$.additional_fun_parameters <- NULL # After extraction we do not need them anymore

  # Check if compare_with_ref_group is TRUE but no ref col is set
  if (isTRUE(dots_extra_args$compare_with_ref_group) &&
    all(
      length(dots_extra_args[[".ref_group"]]) == 0, # only used for testing
      length(extra_afun_params[[".ref_group"]]) == 0
    )
  ) {
    stop(
      "For comparison (compare_with_ref_group = TRUE), the reference group must be specified.",
      "\nSee ref_group in split_cols_by()."
    )
  }

  # Main statistical functions application
  x_stats <- .apply_stat_functions(
    default_stat_fnc = s_summary,
    custom_stat_fnc_list = custom_stat_functions,
    args_list = c(
      x = list(x),
      extra_afun_params,
      dots_extra_args
    )
  )

  # Fill in with stats defaults if needed
  met_grp <- paste0(c("analyze_vars", type), collapse = "_")
  .stats <- c(
    get_stats(met_grp,
      stats_in = .stats,
      add_pval = dots_extra_args$compare_with_ref_group %||% FALSE
    ),
    names(custom_stat_functions) # Additional stats from custom functions
  )

  x_stats <- x_stats[.stats]

  is_char <- is.character(x) || is.factor(x)
  if (is_char) {
    levels_per_stats <- lapply(x_stats, names)
  } else {
    levels_per_stats <- names(x_stats) %>%
      as.list() %>%
      setNames(names(x_stats))
  }

  # Fill in formats/indents/labels with custom input and defaults
  .formats <- get_formats_from_stats(.stats, .formats, levels_per_stats)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods, levels_per_stats)
  lbls <- get_labels_from_stats(.stats, .labels, levels_per_stats)

  if (is_char) {
    # Keep pval_counts stat if present from comparisons and empty
    if ("pval_counts" %in% names(x_stats) && length(x_stats[["pval_counts"]]) == 0) {
      x_stats[["pval_counts"]] <- list(NULL) %>% setNames("pval_counts")
    }

    # Unlist stats
    x_stats <- x_stats %>%
      .unlist_keep_nulls() %>%
      setNames(names(.formats))
  }

  # Check for custom labels from control_analyze_vars
  .labels <- if ("control" %in% names(dots_extra_args)) {
    labels_use_control(lbls, dots_extra_args[["control"]], .labels)
  } else {
    lbls
  }

  # Auto format handling
  .formats <- apply_auto_formatting(
    .formats,
    x_stats,
    extra_afun_params$.df_row,
    extra_afun_params$.var
  )

  # Get and check statistical names from defaults
  .stat_names <- get_stat_names(x_stats, .stat_names) # note is x_stats

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = names(.labels),
    .stat_names = .stat_names,
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls()
  )
}

#' @describeIn analyze_variables Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @param ... additional arguments passed to `s_summary()`, including:
#'   * `denom`: (`string`) See parameter description below.
#'   * `.N_row`: (`numeric(1)`) Row-wise N (row group count) for the group of observations being analyzed (i.e. with no
#'     column-based subsetting).
#'   * `.N_col`: (`numeric(1)`) Column-wise N (column count) for the full column being tabulated within.
#'   * `verbose`: (`flag`) Whether additional warnings and messages should be printed. Mainly used to print out
#'     information about factor casting. Defaults to `TRUE`. Used for `character`/`factor` variables only.
#' @param compare_with_ref_group (logical)\cr whether to compare the variable with a reference group.
#' @param .indent_mods (named `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @return
#' * `analyze_vars()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_summary()` to the table layout.
#'
#' @examples
#' ## Fabricated dataset.
#' dta_test <- data.frame(
#'   USUBJID = rep(1:6, each = 3),
#'   PARAMCD = rep("lab", 6 * 3),
#'   AVISIT  = rep(paste0("V", 1:3), 6),
#'   ARM     = rep(LETTERS[1:3], rep(6, 3)),
#'   AVAL    = c(9:1, rep(NA, 9))
#' )
#'
#' # `analyze_vars()` in `rtables` pipelines
#' ## Default output within a `rtables` pipeline.
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(vars = "AVAL")
#'
#' build_table(l, df = dta_test)
#'
#' ## Select and format statistics output.
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(
#'     vars = "AVAL",
#'     .stats = c("n", "mean_sd", "quantiles"),
#'     .formats = c("mean_sd" = "xx.x, xx.x"),
#'     .labels = c(n = "n", mean_sd = "Mean, SD", quantiles = c("Q1 - Q3"))
#'   )
#'
#' build_table(l, df = dta_test)
#'
#' ## Use arguments interpreted by `s_summary`.
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   split_rows_by(var = "AVISIT") %>%
#'   analyze_vars(vars = "AVAL", na_rm = FALSE)
#'
#' build_table(l, df = dta_test)
#'
#' ## Handle `NA` levels first when summarizing factors.
#' dta_test$AVISIT <- NA_character_
#' dta_test <- df_explicit_na(dta_test)
#' l <- basic_table() %>%
#'   split_cols_by(var = "ARM") %>%
#'   analyze_vars(vars = "AVISIT", na_rm = FALSE)
#'
#' build_table(l, df = dta_test)
#'
#' # auto format
#' dt <- data.frame("VAR" = c(0.001, 0.2, 0.0011000, 3, 4))
#' basic_table() %>%
#'   analyze_vars(
#'     vars = "VAR",
#'     .stats = c("n", "mean", "mean_sd", "range"),
#'     .formats = c("mean_sd" = "auto", "range" = "auto")
#'   ) %>%
#'   build_table(dt)
#'
#' @export
#' @order 2
analyze_vars <- function(lyt,
                         vars,
                         var_labels = vars,
                         na_str = default_na_str(),
                         nested = TRUE,
                         show_labels = "default",
                         table_names = vars,
                         section_div = NA_character_,
                         ...,
                         na_rm = TRUE,
                         compare_with_ref_group = FALSE,
                         .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
                         .stat_names = NULL,
                         .formats = NULL,
                         .labels = NULL,
                         .indent_mods = NULL) {
  # Depending on main functions
  extra_args <- list(
    "na_rm" = na_rm,
    "compare_with_ref_group" = compare_with_ref_group,
    ...
  )

  # Needed defaults
  if (!is.null(.stats)) extra_args[[".stats"]] <- .stats
  if (!is.null(.stat_names)) extra_args[[".stat_names"]] <- .stat_names
  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  # Adding all additional information from layout to analysis functions (see ?rtables::additional_fun_params)
  extra_args[[".additional_fun_parameters"]] <- get_additional_afun_params(add_alt_df = FALSE)
  formals(a_summary) <- c(
    formals(a_summary),
    extra_args[[".additional_fun_parameters"]]
  )

  # Main {rtables} structural call
  analyze(
    lyt = lyt,
    vars = vars,
    var_labels = var_labels,
    afun = a_summary,
    na_str = na_str,
    inclNAs = !na_rm,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names,
    section_div = section_div
  )
}

#' Count patients with toxicity grades that have worsened from baseline by highest grade post-baseline
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_abnormal_lab_worsen_by_baseline()] creates a layout element to count patients with
#' analysis toxicity grades which have worsened from baseline, categorized by highest (worst) grade post-baseline.
#'
#' This function analyzes primary analysis variable `var` which indicates analysis toxicity grades. Additional
#' analysis variables that can be supplied as a list via the `variables` parameter are `id` (defaults to `USUBJID`),
#' a variable to indicate unique subject identifiers, `baseline_var` (defaults to `BTOXGR`), a variable to indicate
#' baseline toxicity grades, and `direction_var` (defaults to `GRADDIR`), a variable to indicate toxicity grade
#' directions of interest to include (e.g. `"H"` (high), `"L"` (low), or `"B"` (both)).
#'
#' For the direction(s) specified in `direction_var`, patient counts by worst grade for patients who have
#' worsened from baseline are calculated as follows:
#'   * `1` to `4`: The number of patients who have worsened from their baseline grades with worst
#'     grades 1-4, respectively.
#'   * `Any`: The total number of patients who have worsened from their baseline grades.
#'
#' Fractions are calculated by dividing the above counts by the number of patients who's analysis toxicity grades
#' have worsened from baseline toxicity grades during treatment.
#'
#' Prior to using this function in your table layout you must use [rtables::split_rows_by()] to create a row
#' split on variable `direction_var`.
#'
#' @inheritParams argument_convention
#' @param variables (named `list` of `string`)\cr list of additional analysis variables including:
#'   * `id` (`string`)\cr subject variable name.
#'   * `baseline_var` (`string`)\cr name of the data column containing baseline toxicity variable.
#'   * `direction_var` (`string`)\cr see `direction_var` for more details.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("abnormal_by_worst_grade_worsen"), type = "sh")``
#'
#' @seealso Relevant helper functions [h_adlb_worsen()] and [h_worsen_counter()] which are used within
#' [s_count_abnormal_lab_worsen_by_baseline()] to process input data.
#'
#' @name abnormal_by_worst_grade_worsen
#' @order 1
NULL

#' Helper function to prepare ADLB with worst labs
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to prepare a `df` for generate the patient count shift table.
#'
#' @param adlb (`data.frame`)\cr ADLB data frame.
#' @param worst_flag_low (named `vector`)\cr worst low post-baseline lab grade flag variable. See how this is
#'   implemented in the following examples.
#' @param worst_flag_high (named `vector`)\cr worst high post-baseline lab grade flag variable. See how this is
#'   implemented in the following examples.
#' @param direction_var (`string`)\cr name of the direction variable specifying the direction of the shift table of
#'   interest. Only lab records flagged by `L`, `H` or `B` are included in the shift table.
#'   * `L`: low direction only
#'   * `H`: high direction only
#'   * `B`: both low and high directions
#'
#' @return `h_adlb_worsen()` returns the `adlb` `data.frame` containing only the
#'   worst labs specified according to `worst_flag_low` or `worst_flag_high` for the
#'   direction specified according to `direction_var`. For instance, for a lab that is
#'   needed for the low direction only, only records flagged by `worst_flag_low` are
#'   selected. For a lab that is needed for both low and high directions, the worst
#'   low records are selected for the low direction, and the worst high record are selected
#'   for the high direction.
#'
#' @seealso [abnormal_by_worst_grade_worsen]
#'
#' @examples
#' library(dplyr)
#'
#' # The direction variable, GRADDR, is based on metadata
#' adlb <- tern_ex_adlb %>%
#'   mutate(
#'     GRADDR = case_when(
#'       PARAMCD == "ALT" ~ "B",
#'       PARAMCD == "CRP" ~ "L",
#'       PARAMCD == "IGA" ~ "H"
#'     )
#'   ) %>%
#'   filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
#'
#' df <- h_adlb_worsen(
#'   adlb,
#'   worst_flag_low = c("WGRLOFL" = "Y"),
#'   worst_flag_high = c("WGRHIFL" = "Y"),
#'   direction_var = "GRADDR"
#' )
#'
#' @export
h_adlb_worsen <- function(adlb,
                          worst_flag_low = NULL,
                          worst_flag_high = NULL,
                          direction_var) {
  checkmate::assert_string(direction_var)
  checkmate::assert_subset(as.character(unique(adlb[[direction_var]])), c("B", "L", "H"))
  assert_df_with_variables(adlb, list("Col" = direction_var))

  if (any(unique(adlb[[direction_var]]) == "H")) {
    assert_df_with_variables(adlb, list("High" = names(worst_flag_high)))
  }

  if (any(unique(adlb[[direction_var]]) == "L")) {
    assert_df_with_variables(adlb, list("Low" = names(worst_flag_low)))
  }

  if (any(unique(adlb[[direction_var]]) == "B")) {
    assert_df_with_variables(
      adlb,
      list(
        "Low" = names(worst_flag_low),
        "High" = names(worst_flag_high)
      )
    )
  }

  # extract patients with worst post-baseline lab, either low or high or both
  worst_flag <- c(worst_flag_low, worst_flag_high)
  col_names <- names(worst_flag)
  filter_values <- worst_flag
  temp <- Map(
    function(x, y) which(adlb[[x]] == y),
    col_names,
    filter_values
  )
  position_satisfy_filters <- Reduce(union, temp)

  # select variables of interest
  adlb_f <- adlb[position_satisfy_filters, ]

  # generate subsets for different directionality
  adlb_f_h <- adlb_f[which(adlb_f[[direction_var]] == "H"), ]
  adlb_f_l <- adlb_f[which(adlb_f[[direction_var]] == "L"), ]
  adlb_f_b <- adlb_f[which(adlb_f[[direction_var]] == "B"), ]

  # for labs requiring both high and low, data is duplicated and will be stacked on top of each other
  adlb_f_b_h <- adlb_f_b
  adlb_f_b_l <- adlb_f_b

  # extract data with worst lab
  if (!is.null(worst_flag_high) && !is.null(worst_flag_low)) {
    # change H to High, L to Low
    adlb_f_h[[direction_var]] <- rep("High", nrow(adlb_f_h))
    adlb_f_l[[direction_var]] <- rep("Low", nrow(adlb_f_l))

    # change, B to High and Low
    adlb_f_b_h[[direction_var]] <- rep("High", nrow(adlb_f_b_h))
    adlb_f_b_l[[direction_var]] <- rep("Low", nrow(adlb_f_b_l))

    adlb_out_h <- adlb_f_h[which(adlb_f_h[[names(worst_flag_high)]] == worst_flag_high), ]
    adlb_out_b_h <- adlb_f_b_h[which(adlb_f_b_h[[names(worst_flag_high)]] == worst_flag_high), ]
    adlb_out_l <- adlb_f_l[which(adlb_f_l[[names(worst_flag_low)]] == worst_flag_low), ]
    adlb_out_b_l <- adlb_f_b_l[which(adlb_f_b_l[[names(worst_flag_low)]] == worst_flag_low), ]

    out <- rbind(adlb_out_h, adlb_out_b_h, adlb_out_l, adlb_out_b_l)
  } else if (!is.null(worst_flag_high)) {
    adlb_f_h[[direction_var]] <- rep("High", nrow(adlb_f_h))
    adlb_f_b_h[[direction_var]] <- rep("High", nrow(adlb_f_b_h))

    adlb_out_h <- adlb_f_h[which(adlb_f_h[[names(worst_flag_high)]] == worst_flag_high), ]
    adlb_out_b_h <- adlb_f_b_h[which(adlb_f_b_h[[names(worst_flag_high)]] == worst_flag_high), ]

    out <- rbind(adlb_out_h, adlb_out_b_h)
  } else if (!is.null(worst_flag_low)) {
    adlb_f_l[[direction_var]] <- rep("Low", nrow(adlb_f_l))
    adlb_f_b_l[[direction_var]] <- rep("Low", nrow(adlb_f_b_l))

    adlb_out_l <- adlb_f_l[which(adlb_f_l[[names(worst_flag_low)]] == worst_flag_low), ]
    adlb_out_b_l <- adlb_f_b_l[which(adlb_f_b_l[[names(worst_flag_low)]] == worst_flag_low), ]

    out <- rbind(adlb_out_l, adlb_out_b_l)
  }

  # label
  formatters::var_labels(out) <- formatters::var_labels(adlb_f, fill = FALSE)
  # NA
  out
}

#' Helper function to analyze patients for `s_count_abnormal_lab_worsen_by_baseline()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to count the number of patients and the fraction of patients according to
#' highest post-baseline lab grade variable `.var`, baseline lab grade variable `baseline_var`,
#' and the direction of interest specified in `direction_var`.
#'
#' @inheritParams argument_convention
#' @inheritParams h_adlb_worsen
#' @param baseline_var (`string`)\cr name of the baseline lab grade variable.
#'
#' @return The counts and fraction of patients
#'   whose worst post-baseline lab grades are worse than their baseline grades, for
#'   post-baseline worst grades "1", "2", "3", "4" and "Any".
#'
#' @seealso [abnormal_by_worst_grade_worsen]
#'
#' @examples
#' library(dplyr)
#'
#' # The direction variable, GRADDR, is based on metadata
#' adlb <- tern_ex_adlb %>%
#'   mutate(
#'     GRADDR = case_when(
#'       PARAMCD == "ALT" ~ "B",
#'       PARAMCD == "CRP" ~ "L",
#'       PARAMCD == "IGA" ~ "H"
#'     )
#'   ) %>%
#'   filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
#'
#' df <- h_adlb_worsen(
#'   adlb,
#'   worst_flag_low = c("WGRLOFL" = "Y"),
#'   worst_flag_high = c("WGRHIFL" = "Y"),
#'   direction_var = "GRADDR"
#' )
#'
#' # `h_worsen_counter`
#' h_worsen_counter(
#'   df %>% filter(PARAMCD == "CRP" & GRADDR == "Low"),
#'   id = "USUBJID",
#'   .var = "ATOXGR",
#'   baseline_var = "BTOXGR",
#'   direction_var = "GRADDR"
#' )
#'
#' @export
h_worsen_counter <- function(df, id, .var, baseline_var, direction_var) {
  checkmate::assert_string(id)
  checkmate::assert_string(.var)
  checkmate::assert_string(baseline_var)
  checkmate::assert_scalar(unique(df[[direction_var]]))
  checkmate::assert_subset(unique(df[[direction_var]]), c("High", "Low"))
  assert_df_with_variables(df, list(val = c(id, .var, baseline_var, direction_var)))

  # remove post-baseline missing
  df <- df[df[[.var]] != "<Missing>", ]

  # obtain directionality
  direction <- unique(df[[direction_var]])

  if (direction == "Low") {
    grade <- -1:-4
    worst_grade <- -4
  } else if (direction == "High") {
    grade <- 1:4
    worst_grade <- 4
  }

  if (nrow(df) > 0) {
    by_grade <- lapply(grade, function(i) {
      # filter baseline values that is less than i or <Missing>
      df_temp <- df[df[[baseline_var]] %in% c((i + sign(i) * -1):(-1 * worst_grade), "<Missing>"), ]
      # num: number of patients with post-baseline worst lab equal to i
      num <- length(unique(df_temp[df_temp[[.var]] %in% i, id, drop = TRUE]))
      # denom: number of patients with baseline values less than i or <missing> and post-baseline in the same direction
      denom <- length(unique(df_temp[[id]]))
      rm(df_temp)
      c(num = num, denom = denom)
    })
  } else {
    by_grade <- lapply(1, function(i) {
      c(num = 0, denom = 0)
    })
  }

  names(by_grade) <- as.character(seq_along(by_grade))

  # baseline grade less 4 or missing
  df_temp <- df[!df[[baseline_var]] %in% worst_grade, ]

  # denom: number of patients with baseline values less than 4 or <missing> and post-baseline in the same direction
  denom <- length(unique(df_temp[, id, drop = TRUE]))

  # condition 1: missing baseline and in the direction of abnormality
  con1 <- which(df_temp[[baseline_var]] == "<Missing>" & df_temp[[.var]] %in% grade)
  df_temp_nm <- df_temp[which(df_temp[[baseline_var]] != "<Missing>" & df_temp[[.var]] %in% grade), ]

  # condition 2: if post-baseline values are present then post-baseline values must be worse than baseline
  if (direction == "Low") {
    con2 <- which(as.numeric(as.character(df_temp_nm[[.var]])) < as.numeric(as.character(df_temp_nm[[baseline_var]])))
  } else {
    con2 <- which(as.numeric(as.character(df_temp_nm[[.var]])) > as.numeric(as.character(df_temp_nm[[baseline_var]])))
  }

  # number of patients satisfy either conditions 1 or 2
  num <- length(unique(df_temp[union(con1, con2), id, drop = TRUE]))

  list(fraction = c(by_grade, list("Any" = c(num = num, denom = denom))))
}

#' @describeIn abnormal_by_worst_grade_worsen Statistics function for patients whose worst post-baseline
#'   lab grades are worse than their baseline grades.
#'
#' @return
#' * `s_count_abnormal_lab_worsen_by_baseline()` returns the counts and fraction of patients whose worst
#'   post-baseline lab grades are worse than their baseline grades, for post-baseline worst grades
#'   "1", "2", "3", "4" and "Any".
#'
#' @keywords internal
s_count_abnormal_lab_worsen_by_baseline <- function(df, # nolint
                                                    .var = "ATOXGR",
                                                    variables = list(
                                                      id = "USUBJID",
                                                      baseline_var = "BTOXGR",
                                                      direction_var = "GRADDR"
                                                    )) {
  checkmate::assert_string(.var)
  checkmate::assert_set_equal(names(variables), c("id", "baseline_var", "direction_var"))
  checkmate::assert_string(variables$id)
  checkmate::assert_string(variables$baseline_var)
  checkmate::assert_string(variables$direction_var)
  assert_df_with_variables(df, c(aval = .var, variables[1:3]))
  assert_list_of_variables(variables)

  h_worsen_counter(df, variables$id, .var, variables$baseline_var, variables$direction_var)
}

#' @describeIn abnormal_by_worst_grade_worsen Formatted analysis function which is used as `afun`
#'   in `count_abnormal_lab_worsen_by_baseline()`.
#'
#' @return
#' * `a_count_abnormal_lab_worsen_by_baseline()` returns the corresponding list with
#'   formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_lab_worsen_by_baseline <- make_afun( # nolint
  s_count_abnormal_lab_worsen_by_baseline,
  .formats = c(fraction = format_fraction),
  .ungroup_stats = "fraction"
)

#' @describeIn abnormal_by_worst_grade_worsen Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_lab_worsen_by_baseline()` returns a layout object suitable for passing to further layouting
#'   functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted
#'   rows containing the statistics from `s_count_abnormal_lab_worsen_by_baseline()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' # The direction variable, GRADDR, is based on metadata
#' adlb <- tern_ex_adlb %>%
#'   mutate(
#'     GRADDR = case_when(
#'       PARAMCD == "ALT" ~ "B",
#'       PARAMCD == "CRP" ~ "L",
#'       PARAMCD == "IGA" ~ "H"
#'     )
#'   ) %>%
#'   filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")
#'
#' df <- h_adlb_worsen(
#'   adlb,
#'   worst_flag_low = c("WGRLOFL" = "Y"),
#'   worst_flag_high = c("WGRHIFL" = "Y"),
#'   direction_var = "GRADDR"
#' )
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   add_colcounts() %>%
#'   split_rows_by("PARAMCD") %>%
#'   split_rows_by("GRADDR") %>%
#'   count_abnormal_lab_worsen_by_baseline(
#'     var = "ATOXGR",
#'     variables = list(
#'       id = "USUBJID",
#'       baseline_var = "BTOXGR",
#'       direction_var = "GRADDR"
#'     )
#'   ) %>%
#'   append_topleft("Direction of Abnormality") %>%
#'   build_table(df = df, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_abnormal_lab_worsen_by_baseline <- function(lyt, # nolint
                                                  var,
                                                  variables = list(
                                                    id = "USUBJID",
                                                    baseline_var = "BTOXGR",
                                                    direction_var = "GRADDR"
                                                  ),
                                                  na_str = default_na_str(),
                                                  nested = TRUE,
                                                  ...,
                                                  table_names = NULL,
                                                  .stats = NULL,
                                                  .formats = NULL,
                                                  .labels = NULL,
                                                  .indent_mods = NULL) {
  checkmate::assert_string(var)

  extra_args <- list(variables = variables, ...)

  afun <- make_afun(
    a_count_abnormal_lab_worsen_by_baseline,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  lyt <- analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = "hidden"
  )

  lyt
}

#' Multivariate logistic regression table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Layout-creating function which summarizes a logistic variable regression for binary outcome with
#' categorical/continuous covariates in model statement. For each covariate category (if categorical)
#' or specified values (if continuous), present degrees of freedom, regression parameter estimate and
#' standard error (SE) relative to reference group or category. Report odds ratios for each covariate
#' category or specified values and corresponding Wald confidence intervals as default but allow user
#' to specify other confidence levels. Report p-value for Wald chi-square test of the null hypothesis
#' that covariate has no effect on response in model containing all specified covariates.
#' Allow option to include one two-way interaction and present similar output for
#' each interaction degree of freedom.
#'
#' @inheritParams argument_convention
#' @param drop_and_remove_str (`string`)\cr string to be dropped and removed.
#'
#' @return A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
#'   Adding this function to an `rtable` layout will add a logistic regression variable summary to the table layout.
#'
#' @note For the formula, the variable names need to be standard `data.frame` column names without
#'   special characters.
#'
#' @examples
#' library(dplyr)
#' library(broom)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' df <- tidy(mod1, conf_level = 0.99)
#' df2 <- tidy(mod2, conf_level = 0.99)
#'
#' # flagging empty strings with "_"
#' df <- df_explicit_na(df, na_level = "_")
#' df2 <- df_explicit_na(df2, na_level = "_")
#'
#' result1 <- basic_table() %>%
#'   summarize_logistic(
#'     conf_level = 0.95,
#'     drop_and_remove_str = "_"
#'   ) %>%
#'   build_table(df = df)
#' result1
#'
#' result2 <- basic_table() %>%
#'   summarize_logistic(
#'     conf_level = 0.95,
#'     drop_and_remove_str = "_"
#'   ) %>%
#'   build_table(df = df2)
#' result2
#'
#' @export
#' @order 1
summarize_logistic <- function(lyt,
                               conf_level,
                               drop_and_remove_str = "",
                               .indent_mods = NULL) {
  # checks
  checkmate::assert_string(drop_and_remove_str)

  sum_logistic_variable_test <- logistic_summary_by_flag("is_variable_summary")
  sum_logistic_term_estimates <- logistic_summary_by_flag("is_term_summary", .indent_mods = .indent_mods)
  sum_logistic_odds_ratios <- logistic_summary_by_flag("is_reference_summary", .indent_mods = .indent_mods)
  split_fun <- drop_and_remove_levels(drop_and_remove_str)

  lyt <- logistic_regression_cols(lyt, conf_level = conf_level)
  lyt <- split_rows_by(lyt, var = "variable", labels_var = "variable_label", split_fun = split_fun)
  lyt <- sum_logistic_variable_test(lyt)
  lyt <- split_rows_by(lyt, var = "term", labels_var = "term_label", split_fun = split_fun)
  lyt <- sum_logistic_term_estimates(lyt)
  lyt <- split_rows_by(lyt, var = "interaction", labels_var = "interaction_label", split_fun = split_fun)
  lyt <- split_rows_by(lyt, var = "reference", labels_var = "reference_label", split_fun = split_fun)
  lyt <- sum_logistic_odds_ratios(lyt)
  lyt
}

#' Fit for logistic regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fit a (conditional) logistic regression model.
#'
#' @inheritParams argument_convention
#' @param data (`data.frame`)\cr the data frame on which the model was fit.
#' @param response_definition (`string`)\cr the definition of what an event is in terms of `response`.
#'   This will be used when fitting the (conditional) logistic regression model on the left hand
#'   side of the formula.
#'
#' @return A fitted logistic regression model.
#'
#' @section Model Specification:
#'
#' The `variables` list needs to include the following elements:
#'   * `arm`: Treatment arm variable name.
#'   * `response`: The response arm variable name. Usually this is a 0/1 variable.
#'   * `covariates`: This is either `NULL` (no covariates) or a character vector of covariate variable names.
#'   * `interaction`: This is either `NULL` (no interaction) or a string of a single covariate variable name already
#'     included in `covariates`. Then the interaction with the treatment arm is included in the model.
#'
#' @examples
#' library(dplyr)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' @export
fit_logistic <- function(data,
                         variables = list(
                           response = "Response",
                           arm = "ARMCD",
                           covariates = NULL,
                           interaction = NULL,
                           strata = NULL
                         ),
                         response_definition = "response") {
  assert_df_with_variables(data, variables)
  checkmate::assert_subset(names(variables), c("response", "arm", "covariates", "interaction", "strata"))
  checkmate::assert_string(response_definition)
  checkmate::assert_true(grepl("response", response_definition))

  response_definition <- sub(
    pattern = "response",
    replacement = variables$response,
    x = response_definition,
    fixed = TRUE
  )
  form <- paste0(response_definition, " ~ ", variables$arm)
  if (!is.null(variables$covariates)) {
    form <- paste0(form, " + ", paste(variables$covariates, collapse = " + "))
  }
  if (!is.null(variables$interaction)) {
    checkmate::assert_string(variables$interaction)
    checkmate::assert_subset(variables$interaction, variables$covariates)
    form <- paste0(form, " + ", variables$arm, ":", variables$interaction)
  }
  if (!is.null(variables$strata)) {
    strata_arg <- if (length(variables$strata) > 1) {
      paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
    } else {
      variables$strata
    }
    form <- paste0(form, "+ strata(", strata_arg, ")")
  }
  formula <- stats::as.formula(form)
  if (is.null(variables$strata)) {
    stats::glm(
      formula = formula,
      data = data,
      family = stats::binomial("logit")
    )
  } else {
    clogit_with_tryCatch(
      formula = formula,
      data = data,
      x = TRUE
    )
  }
}

#' Custom tidy method for binomial GLM results
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper method (for [broom::tidy()]) to prepare a data frame from a `glm` object
#' with `binomial` family.
#'
#' @inheritParams argument_convention
#' @param at (`numeric` or `NULL`)\cr optional values for the interaction variable. Otherwise the median is used.
#' @param x (`glm`)\cr logistic regression model fitted by [stats::glm()] with "binomial" family.
#'
#' @return A `data.frame` containing the tidied model.
#'
#' @method tidy glm
#'
#' @seealso [h_logistic_regression] for relevant helper functions.
#'
#' @examples
#' library(dplyr)
#' library(broom)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   filter(RACE %in% c("ASIAN", "WHITE", "BLACK OR AFRICAN AMERICAN")) %>%
#'   mutate(
#'     Response = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     RACE = factor(RACE),
#'     SEX = factor(SEX)
#'   )
#' formatters::var_labels(adrs_f) <- c(formatters::var_labels(tern_ex_adrs), Response = "Response")
#' mod1 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE")
#'   )
#' )
#' mod2 <- fit_logistic(
#'   data = adrs_f,
#'   variables = list(
#'     response = "Response",
#'     arm = "ARMCD",
#'     covariates = c("AGE", "RACE"),
#'     interaction = "AGE"
#'   )
#' )
#'
#' df <- tidy(mod1, conf_level = 0.99)
#' df2 <- tidy(mod2, conf_level = 0.99)
#'
#' @export
tidy.glm <- function(x, # nolint
                     conf_level = 0.95,
                     at = NULL,
                     ...) {
  checkmate::assert_class(x, "glm")
  checkmate::assert_set_equal(x$family$family, "binomial")

  terms_name <- attr(stats::terms(x), "term.labels")
  xs_class <- attr(x$terms, "dataClasses")
  interaction <- terms_name[which(!terms_name %in% names(xs_class))]
  df <- if (length(interaction) == 0) {
    h_logistic_simple_terms(
      x = terms_name,
      fit_glm = x,
      conf_level = conf_level
    )
  } else {
    h_logistic_inter_terms(
      x = terms_name,
      fit_glm = x,
      conf_level = conf_level,
      at = at
    )
  }
  for (var in c("variable", "term", "interaction", "reference")) {
    df[[var]] <- factor(df[[var]], levels = unique(df[[var]]))
  }
  df
}

#' Logistic regression multivariate column layout function
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Layout-creating function which creates a multivariate column layout summarizing logistic
#' regression results. This function is a wrapper for [rtables::split_cols_by_multivar()].
#'
#' @inheritParams argument_convention
#'
#' @return A layout object suitable for passing to further layouting functions. Adding this
#'   function to an `rtable` layout will split the table into columns corresponding to
#'   statistics `df`, `estimate`, `std_error`, `odds_ratio`, `ci`, and `pvalue`.
#'
#' @export
logistic_regression_cols <- function(lyt,
                                     conf_level = 0.95) {
  vars <- c("df", "estimate", "std_error", "odds_ratio", "ci", "pvalue")
  var_labels <- c(
    df = "Degrees of Freedom",
    estimate = "Parameter Estimate",
    std_error = "Standard Error",
    odds_ratio = "Odds Ratio",
    ci = paste("Wald", f_conf_level(conf_level)),
    pvalue = "p-value"
  )
  split_cols_by_multivar(
    lyt = lyt,
    vars = vars,
    varlabels = var_labels
  )
}

#' Logistic regression summary table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Constructor for content functions to be used in [`summarize_logistic()`] to summarize
#' logistic regression results. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @inheritParams argument_convention
#' @param flag_var (`string`)\cr variable name identifying which row should be used in this
#'   content function.
#'
#' @return A content function.
#'
#' @export
logistic_summary_by_flag <- function(flag_var, na_str = default_na_str(), .indent_mods = NULL) {
  checkmate::assert_string(flag_var)
  function(lyt) {
    cfun_list <- list(
      df = cfun_by_flag("df", flag_var, format = "xx.", .indent_mods = .indent_mods),
      estimate = cfun_by_flag("estimate", flag_var, format = "xx.xxx", .indent_mods = .indent_mods),
      std_error = cfun_by_flag("std_error", flag_var, format = "xx.xxx", .indent_mods = .indent_mods),
      odds_ratio = cfun_by_flag("odds_ratio", flag_var, format = ">999.99", .indent_mods = .indent_mods),
      ci = cfun_by_flag("ci", flag_var, format = format_extreme_values_ci(2L), .indent_mods = .indent_mods),
      pvalue = cfun_by_flag("pvalue", flag_var, format = "x.xxxx | (<0.0001)", .indent_mods = .indent_mods)
    )
    summarize_row_groups(
      lyt = lyt,
      cfun = cfun_list,
      na_str = na_str
    )
  }
}

#' Tabulate biomarker effects on survival by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The [tabulate_survival_biomarkers()] function creates a layout element to tabulate the estimated effects of multiple
#' continuous biomarker variables on survival across subgroups, returning statistics including median survival time and
#' hazard ratio for each population subgroup. The table is created from `df`, a list of data frames returned by
#' [extract_survival_biomarkers()], with the statistics to include specified via the `vars` parameter.
#'
#' A forest plot can be created from the resulting table using the [g_forest()] function.
#'
#' @inheritParams fit_coxreg_multivar
#' @inheritParams survival_duration_subgroups
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr containing all analysis variables, as returned by
#'   [extract_survival_biomarkers()].
#' @param vars (`character`)\cr the names of statistics to be reported among:
#'   * `n_tot_events`: Total number of events per group.
#'   * `n_tot`: Total number of observations per group.
#'   * `median`: Median survival time.
#'   * `hr`: Hazard ratio.
#'   * `ci`: Confidence interval of hazard ratio.
#'   * `pval`: p-value of the effect.
#'   Note, one of the statistics `n_tot` and `n_tot_events`, as well as both `hr` and `ci` are required.
#'
#' @details These functions create a layout starting from a data frame which contains
#'   the required statistics. The tables are then typically used as input for forest plots.
#'
#' @examples
#' library(dplyr)
#'
#' adtte <- tern_ex_adtte
#'
#' # Save variable labels before data processing steps.
#' adtte_labels <- formatters::var_labels(adtte)
#'
#' adtte_f <- adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVALU = as.character(AVALU),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
#' # in multiple regression models containing one covariate `RACE`,
#' # as well as one stratification variable `STRATA1`. The subgroups
#' # are defined by the levels of `BMRKR2`.
#'
#' df <- extract_survival_biomarkers(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     strata = "STRATA1",
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   label_all = "Total Patients",
#'   data = adtte_f
#' )
#' df
#'
#' # Here we group the levels of `BMRKR2` manually.
#' df_grouped <- extract_survival_biomarkers(
#'   variables = list(
#'     tte = "AVAL",
#'     is_event = "is_event",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     strata = "STRATA1",
#'     covariates = "SEX",
#'     subgroups = "BMRKR2"
#'   ),
#'   data = adtte_f,
#'   groups_lists = list(
#'     BMRKR2 = list(
#'       "low" = "LOW",
#'       "low/medium" = c("LOW", "MEDIUM"),
#'       "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
#'     )
#'   )
#' )
#' df_grouped
#'
#' @name survival_biomarkers_subgroups
#' @order 1
NULL

#' Prepare survival data estimates for multiple biomarkers in a single data frame
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Prepares estimates for number of events, patients and median survival times, as well as hazard ratio estimates,
#' confidence intervals and p-values, for multiple biomarkers across population subgroups in a single data frame.
#' `variables` corresponds to the names of variables found in `data`, passed as a named `list` and requires elements
#' `tte`, `is_event`, `biomarkers` (vector of continuous biomarker variables), and optionally `subgroups` and `strata`.
#' `groups_lists` optionally specifies groupings for `subgroups` variables.
#'
#' @inheritParams argument_convention
#' @inheritParams fit_coxreg_multivar
#' @inheritParams survival_duration_subgroups
#'
#' @return A `data.frame` with columns `biomarker`, `biomarker_label`, `n_tot`, `n_tot_events`,
#'   `median`, `hr`, `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`,
#'   `var_label`, and `row_type`.
#'
#' @seealso [h_coxreg_mult_cont_df()] which is used internally, [tabulate_survival_biomarkers()].
#'
#' @export
extract_survival_biomarkers <- function(variables,
                                        data,
                                        groups_lists = list(),
                                        control = control_coxreg(),
                                        label_all = "All Patients") {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `extract_survival_biomarkers() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }

  checkmate::assert_list(variables)
  checkmate::assert_character(variables$subgroups, null.ok = TRUE)
  checkmate::assert_string(label_all)

  # Start with all patients.
  result_all <- h_coxreg_mult_cont_df(
    variables = variables,
    data = data,
    control = control
  )
  result_all$subgroup <- label_all
  result_all$var <- "ALL"
  result_all$var_label <- label_all
  result_all$row_type <- "content"
  if (is.null(variables$subgroups)) {
    # Only return result for all patients.
    result_all
  } else {
    # Add subgroups results.
    l_data <- h_split_by_subgroups(
      data,
      variables$subgroups,
      groups_lists = groups_lists
    )
    l_result <- lapply(l_data, function(grp) {
      result <- h_coxreg_mult_cont_df(
        variables = variables,
        data = grp$df,
        control = control
      )
      result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
      cbind(result, result_labels)
    })
    result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
    result_subgroups$row_type <- "analysis"
    rbind(
      result_all,
      result_subgroups
    )
  }
}

#' @describeIn survival_biomarkers_subgroups Table-creating function which creates a table
#'   summarizing biomarker effects on survival by subgroup.
#'
#' @param label_all `r lifecycle::badge("deprecated")`\cr please assign the `label_all` parameter within the
#'   [extract_survival_biomarkers()] function when creating `df`.
#'
#' @return An `rtables` table summarizing biomarker effects on survival by subgroup.
#'
#' @note In contrast to [tabulate_survival_subgroups()] this tabulation function does
#'   not start from an input layout `lyt`. This is because internally the table is
#'   created by combining multiple subtables.
#'
#' @seealso [h_tab_surv_one_biomarker()] which is used internally, [extract_survival_biomarkers()].
#'
#' @examples
#' ## Table with default columns.
#' tabulate_survival_biomarkers(df)
#'
#' ## Table with a manually chosen set of columns: leave out "pval", reorder.
#' tab <- tabulate_survival_biomarkers(
#'   df = df,
#'   vars = c("n_tot_events", "ci", "n_tot", "median", "hr"),
#'   time_unit = as.character(adtte_f$AVALU[1])
#' )
#'
#' ## Finally produce the forest plot.
#' \donttest{
#' g_forest(tab, xlim = c(0.8, 1.2))
#' }
#'
#' @export
#' @order 2
tabulate_survival_biomarkers <- function(df,
                                         vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
                                         groups_lists = list(),
                                         control = control_coxreg(),
                                         label_all = lifecycle::deprecated(),
                                         time_unit = NULL,
                                         na_str = default_na_str(),
                                         .indent_mods = 0L) {
  if (lifecycle::is_present(label_all)) {
    lifecycle::deprecate_warn(
      "0.9.5", "tabulate_survival_biomarkers(label_all)",
      details = paste(
        "Please assign the `label_all` parameter within the",
        "`extract_survival_biomarkers()` function when creating `df`."
      )
    )
  }

  checkmate::assert_data_frame(df)
  checkmate::assert_character(df$biomarker)
  checkmate::assert_character(df$biomarker_label)
  checkmate::assert_subset(vars, get_stats("tabulate_survival_biomarkers"))

  extra_args <- list(groups_lists = groups_lists, control = control)

  df_subs <- split(df, f = df$biomarker)
  tabs <- lapply(df_subs, FUN = function(df_sub) {
    tab_sub <- h_tab_surv_one_biomarker(
      df = df_sub,
      vars = vars,
      time_unit = time_unit,
      na_str = na_str,
      .indent_mods = .indent_mods,
      extra_args = extra_args
    )
    # Insert label row as first row in table.
    label_at_path(tab_sub, path = row_paths(tab_sub)[[1]][1]) <- df_sub$biomarker_label[1]
    tab_sub
  })
  result <- do.call(rbind, tabs)

  n_tot_ids <- grep("^n_tot", vars)
  hr_id <- match("hr", vars)
  ci_id <- match("ci", vars)
  structure(
    result,
    forest_header = paste0(c("Higher", "Lower"), "\nBetter"),
    col_x = hr_id,
    col_ci = ci_id,
    col_symbol_size = n_tot_ids[1]
  )
}

#' Helper functions for tabulating biomarker effects on binary response by subgroup
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper functions which are documented here separately to not confuse the user
#' when reading about the user-facing functions.
#'
#' @inheritParams response_biomarkers_subgroups
#' @inheritParams extract_rsp_biomarkers
#' @inheritParams argument_convention
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#'
#' adrs <- tern_ex_adrs
#' adrs_labels <- formatters::var_labels(adrs)
#'
#' adrs_f <- adrs %>%
#'   filter(PARAMCD == "BESRSPI") %>%
#'   mutate(rsp = AVALC == "CR")
#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
#'
#' @name h_response_biomarkers_subgroups
NULL

#' @describeIn h_response_biomarkers_subgroups helps with converting the "response" function variable list
#'   to the "logistic regression" variable list. The reason is that currently there is an
#'   inconsistency between the variable names accepted by `extract_rsp_subgroups()` and `fit_logistic()`.
#'
#' @param biomarker (`string`)\cr the name of the biomarker variable.
#'
#' @return
#' * `h_rsp_to_logistic_variables()` returns a named `list` of elements `response`, `arm`, `covariates`, and `strata`.
#'
#' @examples
#' # This is how the variable list is converted internally.
#' h_rsp_to_logistic_variables(
#'   variables = list(
#'     rsp = "RSP",
#'     covariates = c("A", "B"),
#'     strata = "D"
#'   ),
#'   biomarker = "AGE"
#' )
#'
#' @export
h_rsp_to_logistic_variables <- function(variables, biomarker) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_rsp_to_logistic_variables() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }
  checkmate::assert_list(variables)
  checkmate::assert_string(variables$rsp)
  checkmate::assert_string(biomarker)
  list(
    response = variables$rsp,
    arm = biomarker,
    covariates = variables$covariates,
    strata = variables$strata
  )
}

#' @describeIn h_response_biomarkers_subgroups prepares estimates for number of responses, patients and
#'   overall response rate, as well as odds ratio estimates, confidence intervals and p-values, for multiple
#'   biomarkers in a given single data set.
#'   `variables` corresponds to names of variables found in `data`, passed as a named list and requires elements
#'   `rsp` and `biomarkers` (vector of continuous biomarker variables) and optionally `covariates`
#'   and `strata`.
#'
#' @return
#' * `h_logistic_mult_cont_df()` returns a `data.frame` containing estimates and statistics for the selected biomarkers.
#'
#' @examples
#' # For a single population, estimate separately the effects
#' # of two biomarkers.
#' df <- h_logistic_mult_cont_df(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX"
#'   ),
#'   data = adrs_f
#' )
#' df
#'
#' # If the data set is empty, still the corresponding rows with missings are returned.
#' h_coxreg_mult_cont_df(
#'   variables = list(
#'     rsp = "rsp",
#'     biomarkers = c("BMRKR1", "AGE"),
#'     covariates = "SEX",
#'     strata = "STRATA1"
#'   ),
#'   data = adrs_f[NULL, ]
#' )
#'
#' @export
h_logistic_mult_cont_df <- function(variables,
                                    data,
                                    control = control_logistic()) {
  if ("strat" %in% names(variables)) {
    warning(
      "Warning: the `strat` element name of the `variables` list argument to `h_logistic_mult_cont_df() ",
      "was deprecated in tern 0.9.4.\n  ",
      "Please use the name `strata` instead of `strat` in the `variables` argument."
    )
    variables[["strata"]] <- variables[["strat"]]
  }
  assert_df_with_variables(data, variables)

  checkmate::assert_character(variables$biomarkers, min.len = 1, any.missing = FALSE)
  checkmate::assert_list(control, names = "named")

  conf_level <- control[["conf_level"]]
  pval_label <- "p-value (Wald)"

  # If there is any data, run model, otherwise return empty results.
  if (nrow(data) > 0) {
    bm_cols <- match(variables$biomarkers, names(data))
    l_result <- lapply(variables$biomarkers, function(bm) {
      model_fit <- fit_logistic(
        variables = h_rsp_to_logistic_variables(variables, bm),
        data = data,
        response_definition = control$response_definition
      )
      result <- h_logistic_simple_terms(
        x = bm,
        fit_glm = model_fit,
        conf_level = control$conf_level
      )
      resp_vector <- if (inherits(model_fit, "glm")) {
        model_fit$model[[variables$rsp]]
      } else {
        as.logical(as.matrix(model_fit$y)[, "status"])
      }
      data.frame(
        # Dummy column needed downstream to create a nested header.
        biomarker = bm,
        biomarker_label = formatters::var_labels(data[bm], fill = TRUE),
        n_tot = length(resp_vector),
        n_rsp = sum(resp_vector),
        prop = mean(resp_vector),
        or = as.numeric(result[1L, "odds_ratio"]),
        lcl = as.numeric(result[1L, "lcl"]),
        ucl = as.numeric(result[1L, "ucl"]),
        conf_level = conf_level,
        pval = as.numeric(result[1L, "pvalue"]),
        pval_label = pval_label,
        stringsAsFactors = FALSE
      )
    })
    do.call(rbind, args = c(l_result, make.row.names = FALSE))
  } else {
    data.frame(
      biomarker = variables$biomarkers,
      biomarker_label = formatters::var_labels(data[variables$biomarkers], fill = TRUE),
      n_tot = 0L,
      n_rsp = 0L,
      prop = NA,
      or = NA,
      lcl = NA,
      ucl = NA,
      conf_level = conf_level,
      pval = NA,
      pval_label = pval_label,
      row.names = seq_along(variables$biomarkers),
      stringsAsFactors = FALSE
    )
  }
}

#' @describeIn h_response_biomarkers_subgroups Prepares a single sub-table given a `df_sub` containing
#'   the results for a single biomarker.
#'
#' @param df (`data.frame`)\cr results for a single biomarker, as part of what is
#'   returned by [extract_rsp_biomarkers()] (it needs a couple of columns which are
#'   added by that high-level function relative to what is returned by [h_logistic_mult_cont_df()],
#'   see the example).
#'
#' @return
#' * `h_tab_rsp_one_biomarker()` returns an `rtables` table object with the given statistics arranged in columns.
#'
#' @examples
#' # Starting from above `df`, zoom in on one biomarker and add required columns.
#' df1 <- df[1, ]
#' df1$subgroup <- "All patients"
#' df1$row_type <- "content"
#' df1$var <- "ALL"
#' df1$var_label <- "All patients"
#'
#' h_tab_rsp_one_biomarker(
#'   df1,
#'   vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval")
#' )
#'
#' @export
h_tab_rsp_one_biomarker <- function(df,
                                    vars,
                                    na_str = default_na_str(),
                                    .indent_mods = 0L) {
  afuns <- a_response_subgroups(na_str = na_str)[vars]
  colvars <- d_rsp_subgroups_colvars(
    vars,
    conf_level = df$conf_level[1],
    method = df$pval_label[1]
  )
  h_tab_one_biomarker(
    df = df,
    afuns = afuns,
    colvars = colvars,
    na_str = na_str,
    .indent_mods = .indent_mods
  )
}

#' Missing data
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Substitute missing data with a string or factor level.
#'
#' @param x (`factor` or `character`)\cr values for which any missing values should be substituted.
#' @param label (`string`)\cr string that missing data should be replaced with.
#'
#' @return `x` with any `NA` values substituted by `label`.
#'
#' @examples
#' explicit_na(c(NA, "a", "b"))
#' is.na(explicit_na(c(NA, "a", "b")))
#'
#' explicit_na(factor(c(NA, "a", "b")))
#' is.na(explicit_na(factor(c(NA, "a", "b"))))
#'
#' explicit_na(sas_na(c("a", "")))
#'
#' @export
explicit_na <- function(x, label = "<Missing>") {
  checkmate::assert_string(label)

  if (is.factor(x)) {
    x <- forcats::fct_na_value_to_level(x, label)
    forcats::fct_drop(x, only = label)
  } else if (is.character(x)) {
    x[is.na(x)] <- label
    x
  } else {
    stop("only factors and character vectors allowed")
  }
}

#' Convert strings to `NA`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' SAS imports missing data as empty strings or strings with whitespaces only. This helper function can be used to
#' convert these values to `NA`s.
#'
#' @inheritParams explicit_na
#' @param empty (`flag`)\cr if `TRUE`, empty strings get replaced by `NA`.
#' @param whitespaces (`flag`)\cr if `TRUE`, strings made from only whitespaces get replaced with `NA`.
#'
#' @return `x` with `""` and/or whitespace-only values substituted by `NA`, depending on the values of
#'   `empty` and `whitespaces`.
#'
#' @examples
#' sas_na(c("1", "", " ", "   ", "b"))
#' sas_na(factor(c("", " ", "b")))
#'
#' is.na(sas_na(c("1", "", " ", "   ", "b")))
#'
#' @export
sas_na <- function(x, empty = TRUE, whitespaces = TRUE) {
  checkmate::assert_flag(empty)
  checkmate::assert_flag(whitespaces)

  if (is.factor(x)) {
    empty_levels <- levels(x) == ""
    if (empty && any(empty_levels)) levels(x)[empty_levels] <- NA

    ws_levels <- grepl("^\\s+$", levels(x))
    if (whitespaces && any(ws_levels)) levels(x)[ws_levels] <- NA

    x
  } else if (is.character(x)) {
    if (empty) x[x == ""] <- NA_character_

    if (whitespaces) x[grepl("^\\s+$", x)] <- NA_character_

    x
  } else {
    stop("only factors and character vectors allowed")
  }
}

#' Custom split functions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Collection of useful functions that are expanding on the core list of functions
#' provided by `rtables`. See [rtables::custom_split_funs] and [rtables::make_split_fun()]
#' for more information on how to make a custom split function. All these functions
#' work with [rtables::split_rows_by()] argument `split_fun` to modify the way the split
#' happens. For other split functions, consider consulting [`rtables::split_funcs`].
#'
#' @seealso [rtables::make_split_fun()]
#'
#' @name utils_split_funs
NULL

#' @describeIn utils_split_funs Split function to place reference group facet at a specific position
#'  during post-processing stage.
#'
#' @param position (`string` or `integer`)\cr position to use for the reference group facet. Can be `"first"`,
#'   `"last"`, or a specific position.
#'
#' @return
#' * `ref_group_position()` returns an utility function that puts the reference group
#'   as first, last or at a certain position and needs to be assigned to `split_fun`.
#'
#' @examples
#' library(dplyr)
#'
#' dat <- data.frame(
#'   x = factor(letters[1:5], levels = letters[5:1]),
#'   y = 1:5
#' )
#'
#' # With rtables layout functions
#' basic_table() %>%
#'   split_cols_by("x", ref_group = "c", split_fun = ref_group_position("last")) %>%
#'   analyze("y") %>%
#'   build_table(dat)
#'
#' # With tern layout funcitons
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVAL = day2month(AVAL),
#'     is_event = CNSR == 0
#'   )
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position("first")) %>%
#'   add_colcounts() %>%
#'   surv_time(
#'     vars = "AVAL",
#'     var_labels = "Survival Time (Months)",
#'     is_event = "is_event",
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM B", split_fun = ref_group_position(2)) %>%
#'   add_colcounts() %>%
#'   surv_time(
#'     vars = "AVAL",
#'     var_labels = "Survival Time (Months)",
#'     is_event = "is_event",
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
ref_group_position <- function(position = "first") {
  make_split_fun(
    post = list(
      function(splret, spl, fulldf) {
        if (!"ref_group_value" %in% methods::slotNames(spl)) {
          stop("Reference group is undefined.")
        }

        spl_var <- rtables:::spl_payload(spl)
        fulldf[[spl_var]] <- factor(fulldf[[spl_var]])
        init_lvls <- levels(fulldf[[spl_var]])

        if (!all(names(splret$values) %in% init_lvls)) {
          stop("This split function does not work with combination facets.")
        }

        ref_group_pos <- which(init_lvls == rtables:::spl_ref_group(spl))
        pos_choices <- c("first", "last")
        if (checkmate::test_choice(position, pos_choices) && position == "first") {
          pos <- 0
        } else if (checkmate::test_choice(position, pos_choices) && position == "last") {
          pos <- length(init_lvls)
        } else if (checkmate::test_int(position, lower = 1, upper = length(init_lvls))) {
          pos <- position - 1
        } else {
          stop("Wrong input for ref group position. It must be 'first', 'last', or a integer.")
        }

        reord_lvls <- append(init_lvls[-ref_group_pos], init_lvls[ref_group_pos], after = pos)
        ord <- match(reord_lvls, names(splret$values))

        make_split_result(
          splret$values[ord],
          splret$datasplit[ord],
          splret$labels[ord]
        )
      }
    )
  )
}

#' @describeIn utils_split_funs Split function to change level order based on an `integer`
#'   vector or a `character` vector that represent the split variable's factor levels.
#'
#' @param order (`character` or `numeric`)\cr vector of ordering indices for the split facets.
#'
#' @return
#' * `level_order()` returns an utility function that changes the original levels' order,
#'   depending on input `order` and split levels.
#'
#' @examples
#' # level_order --------
#' # Even if default would bring ref_group first, the original order puts it last
#' basic_table() %>%
#'   split_cols_by("Species", split_fun = level_order(c(1, 3, 2))) %>%
#'   analyze("Sepal.Length") %>%
#'   build_table(iris)
#'
#' # character vector
#' new_order <- level_order(levels(iris$Species)[c(1, 3, 2)])
#' basic_table() %>%
#'   split_cols_by("Species", ref_group = "virginica", split_fun = new_order) %>%
#'   analyze("Sepal.Length") %>%
#'   build_table(iris)
#'
#' @export
level_order <- function(order) {
  make_split_fun(
    post = list(
      function(splret, spl, fulldf) {
        if (checkmate::test_integerish(order)) {
          checkmate::assert_integerish(order, lower = 1, upper = length(splret$values))
          ord <- order
        } else {
          checkmate::assert_character(order, len = length(splret$values))
          checkmate::assert_set_equal(order, names(splret$values), ordered = FALSE)
          ord <- match(order, names(splret$values))
        }
        make_split_result(
          splret$values[ord],
          splret$datasplit[ord],
          splret$labels[ord]
        )
      }
    )
  )
}

#' Incidence rate estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [estimate_incidence_rate()] creates a layout element to estimate an event rate adjusted for
#' person-years at risk, otherwise known as incidence rate. The primary analysis variable specified via `vars` is
#' the person-years at risk. In addition to this variable, the `n_events` variable for number of events observed (where
#' a value of 1 means an event was observed and 0 means that no event was observed) must also be specified.
#'
#' @inheritParams argument_convention
#' @param control (`list`)\cr parameters for estimation details, specified by using
#'   the helper function [control_incidence_rate()]. Possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level for the estimated incidence rate.
#'   * `conf_type` (`string`)\cr `normal` (default), `normal_log`, `exact`, or `byar`
#'     for confidence interval type.
#'   * `input_time_unit` (`string`)\cr `day`, `week`, `month`, or `year` (default)
#'     indicating time unit for data input.
#'   * `num_pt_year` (`numeric`)\cr time unit for desired output (in person-years).
#' @param n_events (`string`)\cr name of integer variable indicating whether an event has been observed (1) or not (0).
#' @param id_var (`string`)\cr name of variable used as patient identifier if `"n_unique"` is included in `.stats`.
#'   Defaults to `"USUBJID"`.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("estimate_incidence_rate"), type = "sh")``
#' @param summarize (`flag`)\cr whether the function should act as an analyze function (`summarize = FALSE`), or a
#'   summarize function (`summarize = TRUE`). Defaults to `FALSE`.
#' @param label_fmt (`string`)\cr how labels should be formatted after a row split occurs if `summarize = TRUE`. The
#'   string should use `"%s"` to represent row split levels, and `"%.labels"` to represent labels supplied to the
#'   `.labels` argument. Defaults to `"%s - %.labels"`.
#'
#' @seealso [control_incidence_rate()] and helper functions [h_incidence_rate].
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = as.character(seq(6)),
#'   CNSR = c(0, 1, 1, 0, 0, 0),
#'   AVAL = c(10.1, 20.4, 15.3, 20.8, 18.7, 23.4),
#'   ARM = factor(c("A", "A", "A", "B", "B", "B")),
#'   STRATA1 = factor(c("X", "Y", "Y", "X", "X", "Y"))
#' )
#' df$n_events <- 1 - df$CNSR
#'
#' @name incidence_rate
#' @order 1
NULL

#' @describeIn incidence_rate Statistics function which estimates the incidence rate and the
#'   associated confidence interval.
#'
#' @return
#' * `s_incidence_rate()` returns the following statistics:
#'   - `person_years`: Total person-years at risk.
#'   - `n_events`: Total number of events observed.
#'   - `rate`: Estimated incidence rate.
#'   - `rate_ci`: Confidence interval for the incidence rate.
#'   - `n_unique`: Total number of patients with at least one event observed.
#'   - `n_rate`: Total number of events observed & estimated incidence rate.
#'
#' @keywords internal
s_incidence_rate <- function(df,
                             .var,
                             n_events,
                             is_event = lifecycle::deprecated(),
                             id_var = "USUBJID",
                             control = control_incidence_rate()) {
  if (lifecycle::is_present(is_event)) {
    checkmate::assert_string(is_event)
    lifecycle::deprecate_warn(
      "0.9.6", "s_incidence_rate(is_event)", "s_incidence_rate(n_events)"
    )
    n_events <- is_event
    df[[n_events]] <- as.numeric(df[[is_event]])
  }

  assert_df_with_variables(df, list(tte = .var, n_events = n_events))
  checkmate::assert_string(.var)
  checkmate::assert_string(n_events)
  checkmate::assert_string(id_var)
  checkmate::assert_numeric(df[[.var]], any.missing = FALSE)
  checkmate::assert_integerish(df[[n_events]], any.missing = FALSE)

  n_unique <- n_available(unique(df[[id_var]][df[[n_events]] == 1]))
  input_time_unit <- control$input_time_unit
  num_pt_year <- control$num_pt_year
  conf_level <- control$conf_level
  person_years <- sum(df[[.var]], na.rm = TRUE) * (
    1 * (input_time_unit == "year") +
      1 / 12 * (input_time_unit == "month") +
      1 / 52.14 * (input_time_unit == "week") +
      1 / 365.24 * (input_time_unit == "day")
  )
  n_events <- sum(df[[n_events]], na.rm = TRUE)

  result <- h_incidence_rate(
    person_years,
    n_events,
    control
  )
  list(
    person_years = formatters::with_label(person_years, "Total patient-years at risk"),
    n_events = formatters::with_label(n_events, "Number of adverse events observed"),
    rate = formatters::with_label(result$rate, paste("AE rate per", num_pt_year, "patient-years")),
    rate_ci = formatters::with_label(result$rate_ci, f_conf_level(conf_level)),
    n_unique = formatters::with_label(n_unique, "Total number of patients with at least one adverse event"),
    n_rate = formatters::with_label(
      c(n_events, result$rate),
      paste("Number of adverse events observed (AE rate per", num_pt_year, "patient-years)")
    )
  )
}

#' @describeIn incidence_rate Formatted analysis function which is used as `afun` in `estimate_incidence_rate()`.
#'
#' @return
#' * `a_incidence_rate()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_incidence_rate(
#'   df,
#'   .var = "AVAL",
#'   .df_row = df,
#'   n_events = "n_events"
#' )
#'
#' @export
a_incidence_rate <- function(df,
                             labelstr = "",
                             .var,
                             .df_row,
                             n_events,
                             id_var = "USUBJID",
                             control = control_incidence_rate(),
                             .stats = NULL,
                             .formats = c(
                               "person_years" = "xx.x",
                               "n_events" = "xx",
                               "rate" = "xx.xx",
                               "rate_ci" = "(xx.xx, xx.xx)",
                               "n_unique" = "xx",
                               "n_rate" = "xx (xx.x)"
                             ),
                             .labels = NULL,
                             .indent_mods = NULL,
                             na_str = default_na_str(),
                             label_fmt = "%s - %.labels") {
  checkmate::assert_string(label_fmt)

  x_stats <- s_incidence_rate(
    df = df, .var = .var, n_events = n_events, id_var = id_var, control = control
  )
  if (is.null(unlist(x_stats))) {
    return(NULL)
  }

  # Fill in with defaults
  formats_def <- formals()$.formats %>% eval()
  .formats <- c(.formats, formats_def)[!duplicated(names(c(.formats, formats_def)))]
  labels_def <- sapply(x_stats, \(x) attributes(x)$label)
  .labels <- c(.labels, labels_def)[!duplicated(names(c(.labels, labels_def)))]
  if (nzchar(labelstr) > 0) {
    .labels <- sapply(.labels, \(x) gsub("%.labels", x, gsub("%s", labelstr, label_fmt)))
  }

  # Fill in with formatting defaults if needed
  .stats <- get_stats("estimate_incidence_rate", stats_in = .stats)
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  x_stats <- x_stats[.stats]

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls(),
    .format_na_strs = na_str
  )
}

#' @describeIn incidence_rate Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_incidence_rate()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_incidence_rate()` to the table layout.
#'
#' @examples
#' basic_table(show_colcounts = TRUE) %>%
#'   split_cols_by("ARM") %>%
#'   estimate_incidence_rate(
#'     vars = "AVAL",
#'     n_events = "n_events",
#'     control = control_incidence_rate(
#'       input_time_unit = "month",
#'       num_pt_year = 100
#'     )
#'   ) %>%
#'   build_table(df)
#'
#' # summarize = TRUE
#' basic_table(show_colcounts = TRUE) %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("STRATA1", child_labels = "visible") %>%
#'   estimate_incidence_rate(
#'     vars = "AVAL",
#'     n_events = "n_events",
#'     .stats = c("n_unique", "n_rate"),
#'     summarize = TRUE,
#'     label_fmt = "%.labels"
#'   ) %>%
#'   build_table(df)
#'
#' @export
#' @order 2
estimate_incidence_rate <- function(lyt,
                                    vars,
                                    n_events,
                                    id_var = "USUBJID",
                                    control = control_incidence_rate(),
                                    na_str = default_na_str(),
                                    nested = TRUE,
                                    summarize = FALSE,
                                    label_fmt = "%s - %.labels",
                                    ...,
                                    show_labels = "hidden",
                                    table_names = vars,
                                    .stats = c("person_years", "n_events", "rate", "rate_ci"),
                                    .formats = NULL,
                                    .labels = NULL,
                                    .indent_mods = NULL) {
  extra_args <- c(
    list(.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str),
    list(n_events = n_events, id_var = id_var, control = control, label_fmt = label_fmt, ...)
  )

  if (!summarize) {
    analyze(
      lyt,
      vars,
      show_labels = show_labels,
      table_names = table_names,
      afun = a_incidence_rate,
      na_str = na_str,
      nested = nested,
      extra_args = extra_args
    )
  } else {
    summarize_row_groups(
      lyt,
      vars,
      cfun = a_incidence_rate,
      na_str = na_str,
      extra_args = extra_args
    )
  }
}

#' Cox proportional hazards regression
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Fits a Cox regression model and estimates hazard ratio to describe the effect size in a survival analysis.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("summarize_coxreg"), type = "sh")``
#'
#' @details Cox models are the most commonly used methods to estimate the magnitude of
#'   the effect in survival analysis. It assumes proportional hazards: the ratio
#'   of the hazards between groups (e.g., two arms) is constant over time.
#'   This ratio is referred to as the "hazard ratio" (HR) and is one of the
#'   most commonly reported metrics to describe the effect size in survival
#'   analysis (NEST Team, 2020).
#'
#' @seealso [fit_coxreg] for relevant fitting functions, [h_cox_regression] for relevant
#'   helper functions, and [tidy_coxreg] for custom tidy methods.
#'
#' @examples
#' library(survival)
#'
#' # Testing dataset [survival::bladder].
#' set.seed(1, kind = "Mersenne-Twister")
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   tibble::tibble(
#'     TIME = stop,
#'     STATUS = event,
#'     ARM = as.factor(rx),
#'     COVAR1 = as.factor(enum) %>% formatters::with_label("A Covariate Label"),
#'     COVAR2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4, labels = c("F", "F", "M", "M")
#'     ) %>% formatters::with_label("Sex (F/M)")
#'   )
#' )
#' dta_bladder$AGE <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#' dta_bladder$STUDYID <- factor("X")
#'
#' u1_variables <- list(
#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
#' )
#'
#' u2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
#'
#' m1_variables <- list(
#'   time = "TIME", event = "STATUS", arm = "ARM", covariates = c("COVAR1", "COVAR2")
#' )
#'
#' m2_variables <- list(time = "TIME", event = "STATUS", covariates = c("COVAR1", "COVAR2"))
#'
#' @name cox_regression
#' @order 1
NULL

#' @describeIn cox_regression Statistics function that transforms results tabulated
#'   from [fit_coxreg_univar()] or [fit_coxreg_multivar()] into a list.
#'
#' @param model_df (`data.frame`)\cr contains the resulting model fit from a [fit_coxreg]
#'   function with tidying applied via [broom::tidy()].
#' @param .stats (`character`)\cr the names of statistics to be reported among:
#'   * `n`: number of observations (univariate only)
#'   * `hr`: hazard ratio
#'   * `ci`: confidence interval
#'   * `pval`: p-value of the treatment effect
#'   * `pval_inter`: p-value of the interaction effect between the treatment and the covariate (univariate only)
#' @param .which_vars (`character`)\cr which rows should statistics be returned for from the given model.
#'   Defaults to `"all"`. Other options include `"var_main"` for main effects, `"inter"` for interaction effects,
#'   and `"multi_lvl"` for multivariate model covariate level rows. When `.which_vars` is `"all"`, specific
#'   variables can be selected by specifying `.var_nms`.
#' @param .var_nms (`character`)\cr the `term` value of rows in `df` for which `.stats` should be returned. Typically
#'   this is the name of a variable. If using variable labels, `var` should be a vector of both the desired
#'   variable name and the variable label in that order to see all `.stats` related to that variable. When `.which_vars`
#'   is `"var_main"`, `.var_nms` should be only the variable name.
#'
#' @return
#' * `s_coxreg()` returns the selected statistic for from the Cox regression model for the selected variable(s).
#'
#' @examples
#' # s_coxreg
#'
#' # Univariate
#' univar_model <- fit_coxreg_univar(variables = u1_variables, data = dta_bladder)
#' df1 <- broom::tidy(univar_model)
#'
#' s_coxreg(model_df = df1, .stats = "hr")
#'
#' # Univariate with interactions
#' univar_model_inter <- fit_coxreg_univar(
#'   variables = u1_variables, control = control_coxreg(interaction = TRUE), data = dta_bladder
#' )
#' df1_inter <- broom::tidy(univar_model_inter)
#'
#' s_coxreg(model_df = df1_inter, .stats = "hr", .which_vars = "inter", .var_nms = "COVAR1")
#'
#' # Univariate without treatment arm - only "COVAR2" covariate effects
#' univar_covs_model <- fit_coxreg_univar(variables = u2_variables, data = dta_bladder)
#' df1_covs <- broom::tidy(univar_covs_model)
#'
#' s_coxreg(model_df = df1_covs, .stats = "hr", .var_nms = c("COVAR2", "Sex (F/M)"))
#'
#' # Multivariate.
#' multivar_model <- fit_coxreg_multivar(variables = m1_variables, data = dta_bladder)
#' df2 <- broom::tidy(multivar_model)
#'
#' s_coxreg(model_df = df2, .stats = "pval", .which_vars = "var_main", .var_nms = "COVAR1")
#' s_coxreg(
#'   model_df = df2, .stats = "pval", .which_vars = "multi_lvl",
#'   .var_nms = c("COVAR1", "A Covariate Label")
#' )
#'
#' # Multivariate without treatment arm - only "COVAR1" main effect
#' multivar_covs_model <- fit_coxreg_multivar(variables = m2_variables, data = dta_bladder)
#' df2_covs <- broom::tidy(multivar_covs_model)
#'
#' s_coxreg(model_df = df2_covs, .stats = "hr")
#'
#' @export
s_coxreg <- function(model_df, .stats, .which_vars = "all", .var_nms = NULL) {
  assert_df_with_variables(model_df, list(term = "term", stat = .stats))
  checkmate::assert_multi_class(model_df$term, classes = c("factor", "character"))
  model_df$term <- as.character(model_df$term)
  .var_nms <- .var_nms[!is.na(.var_nms)]

  if (length(.var_nms) > 0) model_df <- model_df[model_df$term %in% .var_nms, ]
  if (.which_vars == "multi_lvl") model_df$term <- tail(.var_nms, 1)

  # We need a list with names corresponding to the stats to display of equal length to the list of stats.
  y <- split(model_df, f = model_df$term, drop = FALSE)
  y <- stats::setNames(y, nm = rep(.stats, length(y)))

  if (.which_vars == "var_main") {
    y <- lapply(y, function(x) x[1, ]) # only main effect
  } else if (.which_vars %in% c("inter", "multi_lvl")) {
    y <- lapply(y, function(x) if (nrow(y[[1]]) > 1) x[-1, ] else x) # exclude main effect
  }

  lapply(
    X = y,
    FUN = function(x) {
      z <- as.list(x[[.stats]])
      stats::setNames(z, nm = x$term_label)
    }
  )
}

#' @describeIn cox_regression Analysis function which is used as `afun` in [rtables::analyze()]
#'   and `cfun` in [rtables::summarize_row_groups()] within `summarize_coxreg()`.
#'
#' @param eff (`flag`)\cr whether treatment effect should be calculated. Defaults to `FALSE`.
#' @param var_main (`flag`)\cr whether main effects should be calculated. Defaults to `FALSE`.
#' @param na_str (`string`)\cr custom string to replace all `NA` values with. Defaults to `""`.
#' @param cache_env (`environment`)\cr an environment object used to cache the regression model in order to
#'   avoid repeatedly fitting the same model for every row in the table. Defaults to `NULL` (no caching).
#' @param varlabels (`list`)\cr a named list corresponds to the names of variables found in data, passed
#'   as a named list and corresponding to time, event, arm, strata, and covariates terms. If arm is missing
#'   from variables, then only Cox model(s) including the covariates will be fitted and the corresponding
#'   effect estimates will be tabulated later.
#'
#' @return
#' * `a_coxreg()` returns formatted [rtables::CellValue()].
#'
#' @examples
#' a_coxreg(
#'   df = dta_bladder,
#'   labelstr = "Label 1",
#'   variables = u1_variables,
#'   .spl_context = list(value = "COVAR1"),
#'   .stats = "n",
#'   .formats = "xx"
#' )
#'
#' a_coxreg(
#'   df = dta_bladder,
#'   labelstr = "",
#'   variables = u1_variables,
#'   .spl_context = list(value = "COVAR2"),
#'   .stats = "pval",
#'   .formats = "xx.xxxx"
#' )
#'
#' @export
a_coxreg <- function(df,
                     labelstr,
                     eff = FALSE,
                     var_main = FALSE,
                     multivar = FALSE,
                     variables,
                     at = list(),
                     control = control_coxreg(),
                     .spl_context,
                     .stats,
                     .formats,
                     .indent_mods = NULL,
                     na_str = "",
                     cache_env = NULL) {
  cov_no_arm <- !multivar && !"arm" %in% names(variables) && control$interaction # special case: univar no arm
  cov <- tail(.spl_context$value, 1) # current variable/covariate
  var_lbl <- formatters::var_labels(df)[cov] # check for df labels
  if (length(labelstr) > 1) {
    labelstr <- if (cov %in% names(labelstr)) labelstr[[cov]] else var_lbl # use df labels if none
  } else if (!is.na(var_lbl) && labelstr == cov && cov %in% variables$covariates) {
    labelstr <- var_lbl
  }
  if (eff || multivar || cov_no_arm) {
    control$interaction <- FALSE
  } else {
    variables$covariates <- cov
    if (var_main) control$interaction <- TRUE
  }

  if (is.null(cache_env[[cov]])) {
    if (!multivar) {
      model <- fit_coxreg_univar(variables = variables, data = df, at = at, control = control) %>% broom::tidy()
    } else {
      model <- fit_coxreg_multivar(variables = variables, data = df, control = control) %>% broom::tidy()
    }
    cache_env[[cov]] <- model
  } else {
    model <- cache_env[[cov]]
  }
  if (!multivar && !var_main) model[, "pval_inter"] <- NA_real_

  if (cov_no_arm || (!cov_no_arm && !"arm" %in% names(variables) && is.numeric(df[[cov]]))) {
    multivar <- TRUE
    if (!cov_no_arm) var_main <- TRUE
  }

  vars_coxreg <- list(which_vars = "all", var_nms = NULL)
  if (eff) {
    if (multivar && !var_main) { # multivar treatment level
      var_lbl_arm <- formatters::var_labels(df)[[variables$arm]]
      vars_coxreg[c("var_nms", "which_vars")] <- list(c(variables$arm, var_lbl_arm), "multi_lvl")
    } else { # treatment effect
      vars_coxreg["var_nms"] <- variables$arm
      if (var_main) vars_coxreg["which_vars"] <- "var_main"
    }
  } else {
    if (!multivar || (multivar && var_main && !is.numeric(df[[cov]]))) { # covariate effect/level
      vars_coxreg[c("var_nms", "which_vars")] <- list(cov, "var_main")
    } else if (multivar) { # multivar covariate level
      vars_coxreg[c("var_nms", "which_vars")] <- list(c(cov, var_lbl), "multi_lvl")
      if (var_main) model[cov, .stats] <- NA_real_
    }
    if (!multivar && !var_main && control$interaction) vars_coxreg["which_vars"] <- "inter" # interaction effect
  }
  var_vals <- s_coxreg(model, .stats, .which_vars = vars_coxreg$which_vars, .var_nms = vars_coxreg$var_nms)[[1]]
  var_names <- if (all(grepl("\\(reference = ", names(var_vals))) && labelstr != tail(.spl_context$value, 1)) {
    paste(c(labelstr, tail(strsplit(names(var_vals), " ")[[1]], 3)), collapse = " ") # "reference" main effect labels
  } else if ((!multivar && !eff && !(!var_main && control$interaction) && nchar(labelstr) > 0) ||
    (multivar && var_main && is.numeric(df[[cov]]))) { # nolint
    labelstr # other main effect labels
  } else if (multivar && !eff && !var_main && is.numeric(df[[cov]])) {
    "All" # multivar numeric covariate
  } else {
    names(var_vals)
  }
  in_rows(
    .list = var_vals, .names = var_names, .labels = var_names, .indent_mods = .indent_mods,
    .formats = stats::setNames(rep(.formats, length(var_names)), var_names),
    .format_na_strs = stats::setNames(rep(na_str, length(var_names)), var_names)
  )
}

#' @describeIn cox_regression Layout-creating function which creates a Cox regression summary table
#'   layout. This function is a wrapper for several `rtables` layouting functions. This function
#'   is a wrapper for [rtables::analyze_colvars()] and [rtables::summarize_row_groups()].
#'
#' @inheritParams fit_coxreg_univar
#' @param multivar (`flag`)\cr whether multivariate Cox regression should run (defaults to `FALSE`), otherwise
#'   univariate Cox regression will run.
#' @param common_var (`string`)\cr the name of a factor variable in the dataset which takes the same value
#'   for all rows. This should be created during pre-processing if no such variable currently exists.
#' @param .section_div (`string` or `NA`)\cr string which should be repeated as a section divider between sections.
#'   Defaults to `NA` for no section divider. If a vector of two strings are given, the first will be used between
#'   treatment and covariate sections and the second between different covariates.
#'
#' @return
#' * `summarize_coxreg()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add a Cox regression table
#'   containing the chosen statistics to the table layout.
#'
#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()] which also take the `variables`, `data`,
#'   `at` (univariate only), and `control` arguments but return unformatted univariate and multivariate
#'   Cox regression models, respectively.
#'
#' @examples
#' # summarize_coxreg
#'
#' result_univar <- basic_table() %>%
#'   summarize_coxreg(variables = u1_variables) %>%
#'   build_table(dta_bladder)
#' result_univar
#'
#' result_univar_covs <- basic_table() %>%
#'   summarize_coxreg(
#'     variables = u2_variables,
#'   ) %>%
#'   build_table(dta_bladder)
#' result_univar_covs
#'
#' result_multivar <- basic_table() %>%
#'   summarize_coxreg(
#'     variables = m1_variables,
#'     multivar = TRUE,
#'   ) %>%
#'   build_table(dta_bladder)
#' result_multivar
#'
#' result_multivar_covs <- basic_table() %>%
#'   summarize_coxreg(
#'     variables = m2_variables,
#'     multivar = TRUE,
#'     varlabels = c("Covariate 1", "Covariate 2") # custom labels
#'   ) %>%
#'   build_table(dta_bladder)
#' result_multivar_covs
#'
#' @export
#' @order 2
summarize_coxreg <- function(lyt,
                             variables,
                             control = control_coxreg(),
                             at = list(),
                             multivar = FALSE,
                             common_var = "STUDYID",
                             .stats = c("n", "hr", "ci", "pval", "pval_inter"),
                             .formats = c(
                               n = "xx", hr = "xx.xx", ci = "(xx.xx, xx.xx)",
                               pval = "x.xxxx | (<0.0001)", pval_inter = "x.xxxx | (<0.0001)"
                             ),
                             varlabels = NULL,
                             .indent_mods = NULL,
                             na_str = "",
                             .section_div = NA_character_) {
  if (multivar && control$interaction) {
    warning(paste(
      "Interactions are not available for multivariate cox regression using summarize_coxreg.",
      "The model will be calculated without interaction effects."
    ))
  }
  if (control$interaction && !"arm" %in% names(variables)) {
    stop("To include interactions please specify 'arm' in variables.")
  }

  .stats <- if (!"arm" %in% names(variables) || multivar) { # only valid statistics
    intersect(c("hr", "ci", "pval"), .stats)
  } else if (control$interaction) {
    intersect(c("n", "hr", "ci", "pval", "pval_inter"), .stats)
  } else {
    intersect(c("n", "hr", "ci", "pval"), .stats)
  }
  stat_labels <- c(
    n = "n", hr = "Hazard Ratio", ci = paste0(control$conf_level * 100, "% CI"),
    pval = "p-value", pval_inter = "Interaction p-value"
  )
  stat_labels <- stat_labels[names(stat_labels) %in% .stats]
  .formats <- .formats[names(.formats) %in% .stats]
  env <- new.env() # create caching environment

  lyt <- lyt %>%
    split_cols_by_multivar(
      vars = rep(common_var, length(.stats)),
      varlabels = stat_labels,
      extra_args = list(
        .stats = .stats, .formats = .formats, .indent_mods = .indent_mods, na_str = rep(na_str, length(.stats)),
        cache_env = replicate(length(.stats), list(env))
      )
    )

  if ("arm" %in% names(variables)) { # treatment effect
    lyt <- lyt %>%
      split_rows_by(
        common_var,
        split_label = "Treatment:",
        label_pos = "visible",
        child_labels = "hidden",
        section_div = head(.section_div, 1)
      )
    if (!multivar) {
      lyt <- lyt %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar,
            labelstr = ""
          )
        )
    } else { # treatment level effects
      lyt <- lyt %>%
        summarize_row_groups(
          cfun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, control = control, multivar = multivar, eff = TRUE, var_main = multivar
          )
        ) %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(eff = TRUE, control = control, variables = variables, multivar = multivar, labelstr = "")
        )
    }
  }

  if ("covariates" %in% names(variables)) { # covariate main effects
    lyt <- lyt %>%
      split_rows_by_multivar(
        vars = variables$covariates,
        varlabels = varlabels,
        split_label = "Covariate:",
        nested = FALSE,
        child_labels = if (multivar || control$interaction || !"arm" %in% names(variables)) "default" else "hidden",
        section_div = tail(.section_div, 1)
      )
    if (multivar || control$interaction || !"arm" %in% names(variables)) {
      lyt <- lyt %>%
        summarize_row_groups(
          cfun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, at = at, control = control, multivar = multivar,
            var_main = if (multivar) multivar else control$interaction
          )
        )
    } else {
      if (!is.null(varlabels)) names(varlabels) <- variables$covariates
      lyt <- lyt %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(
            variables = variables, at = at, control = control, multivar = multivar,
            var_main = if (multivar) multivar else control$interaction,
            labelstr = if (is.null(varlabels)) "" else varlabels
          )
        )
    }

    if (!"arm" %in% names(variables)) control$interaction <- TRUE # special case: univar no arm
    if (multivar || control$interaction) { # covariate level effects
      lyt <- lyt %>%
        analyze_colvars(
          afun = a_coxreg,
          na_str = na_str,
          extra_args = list(variables = variables, at = at, control = control, multivar = multivar, labelstr = ""),
          indent_mod = if (!"arm" %in% names(variables) || multivar) 0L else -1L
        )
    }
  }

  lyt
}

#' Helper functions for incidence rate
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param control (`list`)\cr parameters for estimation details, specified by using
#'   the helper function [control_incidence_rate()]. Possible parameter options are:
#'   * `conf_level`: (`proportion`)\cr confidence level for the estimated incidence rate.
#'   * `conf_type`: (`string`)\cr `normal` (default), `normal_log`, `exact`, or `byar`
#'     for confidence interval type.
#'   * `input_time_unit`: (`string`)\cr `day`, `week`, `month`, or `year` (default)
#'     indicating time unit for data input.
#'   * `num_pt_year`: (`numeric`)\cr time unit for desired output (in person-years).
#' @param person_years (`numeric(1)`)\cr total person-years at risk.
#' @param alpha (`numeric(1)`)\cr two-sided alpha-level for confidence interval.
#' @param n_events (`integer(1)`)\cr number of events observed.
#'
#' @return Estimated incidence rate, `rate`, and associated confidence interval, `rate_ci`.
#'
#' @seealso [incidence_rate]
#'
#' @name h_incidence_rate
NULL

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated confidence interval.
#'
#' @keywords internal
h_incidence_rate <- function(person_years,
                             n_events,
                             control = control_incidence_rate()) {
  alpha <- 1 - control$conf_level
  est <- switch(control$conf_type,
    normal = h_incidence_rate_normal(person_years, n_events, alpha),
    normal_log = h_incidence_rate_normal_log(person_years, n_events, alpha),
    exact = h_incidence_rate_exact(person_years, n_events, alpha),
    byar = h_incidence_rate_byar(person_years, n_events, alpha)
  )

  num_pt_year <- control$num_pt_year
  list(
    rate = est$rate * num_pt_year,
    rate_ci = est$rate_ci * num_pt_year
  )
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated confidence interval based on the normal approximation for the
#'   incidence rate. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_normal(200, 2)
#'
#' @export
h_incidence_rate_normal <- function(person_years,
                                    n_events,
                                    alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  est <- n_events / person_years
  se <- sqrt(est / person_years)
  ci <- est + c(-1, 1) * stats::qnorm(1 - alpha / 2) * se

  list(rate = est, rate_ci = ci)
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated confidence interval based on the normal approximation for the
#'   logarithm of the incidence rate. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_normal_log(200, 2)
#'
#' @export
h_incidence_rate_normal_log <- function(person_years,
                                        n_events,
                                        alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  rate_est <- n_events / person_years
  rate_se <- sqrt(rate_est / person_years)
  lrate_est <- log(rate_est)
  lrate_se <- rate_se / rate_est
  ci <- exp(lrate_est + c(-1, 1) * stats::qnorm(1 - alpha / 2) * lrate_se)

  list(rate = rate_est, rate_ci = ci)
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated exact confidence interval. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_exact(200, 2)
#'
#' @export
h_incidence_rate_exact <- function(person_years,
                                   n_events,
                                   alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  est <- n_events / person_years
  lcl <- stats::qchisq(p = (alpha) / 2, df = 2 * n_events) / (2 * person_years)
  ucl <- stats::qchisq(p = 1 - (alpha) / 2, df = 2 * n_events + 2) / (2 * person_years)

  list(rate = est, rate_ci = c(lcl, ucl))
}

#' @describeIn h_incidence_rate Helper function to estimate the incidence rate and
#'   associated Byar's confidence interval. Unit is one person-year.
#'
#' @examples
#' h_incidence_rate_byar(200, 2)
#'
#' @export
h_incidence_rate_byar <- function(person_years,
                                  n_events,
                                  alpha = 0.05) {
  checkmate::assert_number(person_years)
  checkmate::assert_number(n_events)
  assert_proportion_value(alpha)

  est <- n_events / person_years
  seg_1 <- n_events + 0.5
  seg_2 <- 1 - 1 / (9 * (n_events + 0.5))
  seg_3 <- stats::qnorm(1 - alpha / 2) * sqrt(1 / (n_events + 0.5)) / 3
  lcl <- seg_1 * ((seg_2 - seg_3)^3) / person_years
  ucl <- seg_1 * ((seg_2 + seg_3)^3) / person_years

  list(rate = est, rate_ci = c(lcl, ucl))
}

#' Survival time analysis
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [surv_time()] creates a layout element to analyze survival time by calculating survival time
#' median, median confidence interval, quantiles, and range (for all, censored, or event patients). The primary
#' analysis variable `vars` is the time variable and the secondary analysis variable `is_event` indicates whether or
#' not an event has occurred.
#'
#' @inheritParams argument_convention
#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
#'   [control_surv_time()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for survival time.
#'   * `conf_type` (`string`)\cr confidence interval type. Options are "plain" (default), "log", or "log-log",
#'     see more in [survival::survfit()]. Note option "none" is not supported.
#'   * `quantiles` (`numeric`)\cr vector of length two to specify the quantiles of survival time.
#' @param ref_fn_censor (`flag`)\cr whether referential footnotes indicating censored observations should be printed
#'   when the `range` statistic is included.
#' @param .indent_mods (named `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("surv_time"), type = "sh")``
#'
#' @examples
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVAL = day2month(AVAL),
#'     is_event = CNSR == 0
#'   )
#' df <- adtte_f %>% filter(ARMCD == "ARM A")
#'
#' @name survival_time
#' @order 1
NULL

#' @describeIn survival_time Statistics function which analyzes survival times.
#'
#' @return
#' * `s_surv_time()` returns the statistics:
#'   * `median`: Median survival time.
#'   * `median_ci`: Confidence interval for median time.
#'   * `median_ci_3d`: Median with confidence interval for median time.
#'   * `quantiles`: Survival time for two specified quantiles.
#'   * `quantiles_lower`: quantile with confidence interval for the first specified quantile.
#'   * `quantiles_upper`: quantile with confidence interval for the second specified quantile.
#'   * `range_censor`: Survival time range for censored observations.
#'   * `range_event`: Survival time range for observations with events.
#'   * `range`: Survival time range for all observations.
#'
#' @keywords internal
s_surv_time <- function(df,
                        .var,
                        is_event,
                        control = control_surv_time()) {
  checkmate::assert_string(.var)
  assert_df_with_variables(df, list(tte = .var, is_event = is_event))
  checkmate::assert_numeric(df[[.var]], min.len = 1, any.missing = FALSE)
  checkmate::assert_logical(df[[is_event]], min.len = 1, any.missing = FALSE)

  conf_type <- control$conf_type
  conf_level <- control$conf_level
  quantiles <- control$quantiles

  formula <- stats::as.formula(paste0("survival::Surv(", .var, ", ", is_event, ") ~ 1"))
  srv_fit <- survival::survfit(
    formula = formula,
    data = df,
    conf.int = conf_level,
    conf.type = conf_type
  )
  srv_tab <- summary(srv_fit, extend = TRUE)$table
  srv_qt_tab_pre <- stats::quantile(srv_fit, probs = quantiles)
  srv_qt_tab <- srv_qt_tab_pre$quantile
  range_censor <- range_noinf(df[[.var]][!df[[is_event]]], na.rm = TRUE)
  range_event <- range_noinf(df[[.var]][df[[is_event]]], na.rm = TRUE)
  range <- range_noinf(df[[.var]], na.rm = TRUE)

  names(quantiles) <- as.character(100 * quantiles)
  srv_qt_tab_pre <- unlist(srv_qt_tab_pre)
  srv_qt_ci <- lapply(quantiles, function(x) {
    name <- as.character(100 * x)

    c(
      srv_qt_tab_pre[[paste0("quantile.", name)]],
      srv_qt_tab_pre[[paste0("lower.", name)]],
      srv_qt_tab_pre[[paste0("upper.", name)]]
    )
  })

  list(
    median = formatters::with_label(unname(srv_tab["median"]), "Median"),
    median_ci = formatters::with_label(
      unname(srv_tab[paste0(srv_fit$conf.int, c("LCL", "UCL"))]), f_conf_level(conf_level)
    ),
    quantiles = formatters::with_label(
      unname(srv_qt_tab), paste0(quantiles[1] * 100, "% and ", quantiles[2] * 100, "%-ile")
    ),
    range_censor = formatters::with_label(range_censor, "Range (censored)"),
    range_event = formatters::with_label(range_event, "Range (event)"),
    range = formatters::with_label(range, "Range"),
    median_ci_3d = formatters::with_label(
      c(
        unname(srv_tab["median"]),
        unname(srv_tab[paste0(srv_fit$conf.int, c("LCL", "UCL"))])
      ),
      paste0("Median (", f_conf_level(conf_level), ")")
    ),
    quantiles_lower = formatters::with_label(
      unname(srv_qt_ci[[1]]), paste0(quantiles[1] * 100, "%-ile (", f_conf_level(conf_level), ")")
    ),
    quantiles_upper = formatters::with_label(
      unname(srv_qt_ci[[2]]), paste0(quantiles[2] * 100, "%-ile (", f_conf_level(conf_level), ")")
    )
  )
}

#' @describeIn survival_time Formatted analysis function which is used as `afun` in `surv_time()`.
#'
#' @return
#' * `a_surv_time()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_surv_time(
#'   df,
#'   .df_row = df,
#'   .var = "AVAL",
#'   is_event = "is_event"
#' )
#'
#' @export
a_surv_time <- function(df,
                        labelstr = "",
                        .var = NULL,
                        .df_row = NULL,
                        is_event,
                        control = control_surv_time(),
                        ref_fn_censor = TRUE,
                        .stats = NULL,
                        .formats = NULL,
                        .labels = NULL,
                        .indent_mods = NULL,
                        na_str = default_na_str()) {
  x_stats <- s_surv_time(
    df = df, .var = .var, is_event = is_event, control = control
  )
  rng_censor_lwr <- x_stats[["range_censor"]][1]
  rng_censor_upr <- x_stats[["range_censor"]][2]

  # Use method-specific defaults
  fmts <- c(
    median_ci = "(xx.x, xx.x)", quantiles = "xx.x, xx.x", range = "xx.x to xx.x",
    median_ci_3d = "xx.x (xx.x - xx.x)",
    quantiles_lower = "xx.x (xx.x - xx.x)", quantiles_upper = "xx.x (xx.x - xx.x)"
  )
  lbls <- c(
    median_ci = "95% CI", range = "Range", range_censor = "Range (censored)", range_event = "Range (event)",
    median_ci_3d = "Median (95% CI)",
    quantiles_lower = "25%-ile (95% CI)",
    quantiles_upper = "75%-ile (95% CI)"
  )
  lbls_custom <- .labels
  .formats <- c(.formats, fmts[setdiff(names(fmts), names(.formats))])
  .labels <- c(.labels, lbls[setdiff(names(lbls), names(lbls_custom))])

  # Fill in with formatting defaults if needed
  .stats <- get_stats("surv_time", stats_in = .stats)
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels) %>% labels_use_control(control, lbls_custom)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  x_stats <- x_stats[.stats]

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  cell_fns <- setNames(vector("list", length = length(x_stats)), .labels)
  if ("range" %in% names(x_stats) && ref_fn_censor) {
    if (identical(x_stats[["range"]][1], rng_censor_lwr) && identical(x_stats[["range"]][2], rng_censor_upr)) {
      cell_fns[[.labels[["range"]]]] <- "Censored observations: range minimum & maximum"
    } else if (identical(x_stats[["range"]][1], rng_censor_lwr)) {
      cell_fns[[.labels[["range"]]]] <- "Censored observation: range minimum"
    } else if (identical(x_stats[["range"]][2], rng_censor_upr)) {
      cell_fns[[.labels[["range"]]]] <- "Censored observation: range maximum"
    }
  }

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = .labels,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .cell_footnotes = cell_fns
  )
}

#' @describeIn survival_time Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `surv_time()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_surv_time()` to the table layout.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD") %>%
#'   add_colcounts() %>%
#'   surv_time(
#'     vars = "AVAL",
#'     var_labels = "Survival Time (Months)",
#'     is_event = "is_event",
#'     control = control_surv_time(conf_level = 0.9, conf_type = "log-log")
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
#' @order 2
surv_time <- function(lyt,
                      vars,
                      is_event,
                      control = control_surv_time(),
                      ref_fn_censor = TRUE,
                      na_str = default_na_str(),
                      nested = TRUE,
                      ...,
                      var_labels = "Time to Event",
                      show_labels = "visible",
                      table_names = vars,
                      .stats = c("median", "median_ci", "quantiles", "range"),
                      .formats = NULL,
                      .labels = NULL,
                      .indent_mods = c(median_ci = 1L)) {
  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods,
    is_event = is_event, control = control, ref_fn_censor = ref_fn_censor, ...
  )

  analyze(
    lyt = lyt,
    vars = vars,
    afun = a_surv_time,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Count patients with marked laboratory abnormalities
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_abnormal_by_marked()] creates a layout element to count patients with marked laboratory
#' abnormalities for each direction of abnormality, categorized by parameter value.
#'
#' This function analyzes primary analysis variable `var` which indicates whether a single, replicated,
#' or last marked laboratory abnormality was observed. Levels of `var` to include for each marked lab
#' abnormality (`single` and `last_replicated`) can be supplied via the `category` parameter. Additional
#' analysis variables that can be supplied as a list via the `variables` parameter are `id` (defaults
#' to `USUBJID`), a variable to indicate unique subject identifiers, `param` (defaults to `PARAM`), a
#' variable to indicate parameter values, and `direction` (defaults to `abn_dir`), a variable to indicate
#' abnormality directions.
#'
#' For each combination of `param` and `direction` levels, marked lab abnormality counts are calculated
#' as follows:
#'   * `Single, not last` & `Last or replicated`: The number of patients with `Single, not last`
#'     and `Last or replicated` values, respectively.
#'   * `Any`: The number of patients with either single or replicated marked abnormalities.
#'
#' Fractions are calculated by dividing the above counts by the number of patients with at least one
#' valid measurement recorded during the analysis.
#'
#' Prior to using this function in your table layout you must use [rtables::split_rows_by()] to create two
#' row splits, one on variable `param` and one on variable `direction`.
#'
#' @inheritParams argument_convention
#' @param category (`list`)\cr a list with different marked category names for single
#'   and last or replicated.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("abnormal_by_marked"), type = "sh")``
#'
#' @note `Single, not last` and `Last or replicated` levels are mutually exclusive. If a patient has
#'   abnormalities that meet both the `Single, not last` and `Last or replicated` criteria, then the
#'   patient will be counted only under the `Last or replicated` category.
#'
#' @name abnormal_by_marked
#' @order 1
NULL

#' @describeIn abnormal_by_marked Statistics function for patients with marked lab abnormalities.
#'
#' @return
#' * `s_count_abnormal_by_marked()` returns statistic `count_fraction` with `Single, not last`,
#'   `Last or replicated`, and `Any` results.
#'
#' @keywords internal
s_count_abnormal_by_marked <- function(df,
                                       .var = "AVALCAT1",
                                       .spl_context,
                                       category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
                                       variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")) {
  checkmate::assert_string(.var)
  checkmate::assert_list(variables)
  checkmate::assert_list(category)
  checkmate::assert_subset(names(category), c("single", "last_replicated"))
  checkmate::assert_subset(names(variables), c("id", "param", "direction"))
  checkmate::assert_vector(unique(df[[variables$direction]]), max.len = 1)

  assert_df_with_variables(df, c(aval = .var, variables))
  checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))


  first_row <- .spl_context[.spl_context$split == variables[["param"]], ]
  # Patients in the denominator have at least one post-baseline visit.
  subj <- first_row$full_parent_df[[1]][[variables[["id"]]]]
  subj_cur_col <- subj[first_row$cur_col_subset[[1]]]
  # Some subjects may have a record for high and low directions but
  # should be counted only once.
  denom <- length(unique(subj_cur_col))

  if (denom != 0) {
    subjects_last_replicated <- unique(
      df[df[[.var]] %in% category[["last_replicated"]], variables$id, drop = TRUE]
    )
    subjects_single <- unique(
      df[df[[.var]] %in% category[["single"]], variables$id, drop = TRUE]
    )
    # Subjects who have both single and last/replicated abnormalities are counted in only the last/replicated group.
    subjects_single <- setdiff(subjects_single, subjects_last_replicated)
    n_single <- length(subjects_single)
    n_last_replicated <- length(subjects_last_replicated)
    n_any <- n_single + n_last_replicated
    result <- list(count_fraction = list(
      "Single, not last" = c(n_single, n_single / denom),
      "Last or replicated" = c(n_last_replicated, n_last_replicated / denom),
      "Any Abnormality" = c(n_any, n_any / denom)
    ))
  } else {
    result <- list(count_fraction = list(
      "Single, not last" = c(0, 0),
      "Last or replicated" = c(0, 0),
      "Any Abnormality" = c(0, 0)
    ))
  }

  result
}

#' @describeIn abnormal_by_marked Formatted analysis function which is used as `afun`
#'   in `count_abnormal_by_marked()`.
#'
#' @return
#' * `a_count_abnormal_by_marked()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_by_marked <- make_afun(
  s_count_abnormal_by_marked,
  .formats = c(count_fraction = format_count_fraction)
)

#' @describeIn abnormal_by_marked Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_by_marked()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal_by_marked()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(rep(1, 5), rep(2, 5), rep(1, 5), rep(2, 5))),
#'   ARMCD = factor(c(rep("ARM A", 5), rep("ARM B", 5), rep("ARM A", 5), rep("ARM B", 5))),
#'   ANRIND = factor(c(
#'     "NORMAL", "HIGH", "HIGH", "HIGH HIGH", "HIGH",
#'     "HIGH", "HIGH", "HIGH HIGH", "NORMAL", "HIGH HIGH", "NORMAL", "LOW", "LOW", "LOW LOW", "LOW",
#'     "LOW", "LOW", "LOW LOW", "NORMAL", "LOW LOW"
#'   )),
#'   ONTRTFL = rep(c("", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y"), 2),
#'   PARAMCD = factor(c(rep("CRP", 10), rep("ALT", 10))),
#'   AVALCAT1 = factor(rep(c("", "", "", "SINGLE", "REPLICATED", "", "", "LAST", "", "SINGLE"), 2)),
#'   stringsAsFactors = FALSE
#' )
#'
#' df <- df %>%
#'   mutate(abn_dir = factor(
#'     case_when(
#'       ANRIND == "LOW LOW" ~ "Low",
#'       ANRIND == "HIGH HIGH" ~ "High",
#'       TRUE ~ ""
#'     ),
#'     levels = c("Low", "High")
#'   ))
#'
#' # Select only post-baseline records.
#' df <- df %>% filter(ONTRTFL == "Y")
#' df_crp <- df %>%
#'   filter(PARAMCD == "CRP") %>%
#'   droplevels()
#' full_parent_df <- list(df_crp, "not_needed")
#' cur_col_subset <- list(rep(TRUE, nrow(df_crp)), "not_needed")
#' spl_context <- data.frame(
#'   split = c("PARAMCD", "GRADE_DIR"),
#'   full_parent_df = I(full_parent_df),
#'   cur_col_subset = I(cur_col_subset)
#' )
#'
#' map <- unique(
#'   df[df$abn_dir %in% c("Low", "High") & df$AVALCAT1 != "", c("PARAMCD", "abn_dir")]
#' ) %>%
#'   lapply(as.character) %>%
#'   as.data.frame() %>%
#'   arrange(PARAMCD, abn_dir)
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   split_rows_by("PARAMCD") %>%
#'   summarize_num_patients(
#'     var = "USUBJID",
#'     .stats = "unique_count"
#'   ) %>%
#'   split_rows_by(
#'     "abn_dir",
#'     split_fun = trim_levels_to_map(map)
#'   ) %>%
#'   count_abnormal_by_marked(
#'     var = "AVALCAT1",
#'     variables = list(
#'       id = "USUBJID",
#'       param = "PARAMCD",
#'       direction = "abn_dir"
#'     )
#'   ) %>%
#'   build_table(df = df)
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   split_rows_by("PARAMCD") %>%
#'   summarize_num_patients(
#'     var = "USUBJID",
#'     .stats = "unique_count"
#'   ) %>%
#'   split_rows_by(
#'     "abn_dir",
#'     split_fun = trim_levels_in_group("abn_dir")
#'   ) %>%
#'   count_abnormal_by_marked(
#'     var = "AVALCAT1",
#'     variables = list(
#'       id = "USUBJID",
#'       param = "PARAMCD",
#'       direction = "abn_dir"
#'     )
#'   ) %>%
#'   build_table(df = df)
#'
#' @export
#' @order 2
count_abnormal_by_marked <- function(lyt,
                                     var,
                                     category = list(single = "SINGLE", last_replicated = c("LAST", "REPLICATED")),
                                     variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir"),
                                     na_str = default_na_str(),
                                     nested = TRUE,
                                     ...,
                                     .stats = NULL,
                                     .formats = NULL,
                                     .labels = NULL,
                                     .indent_mods = NULL) {
  checkmate::assert_string(var)

  extra_args <- list(category = category, variables = variables, ...)

  afun <- make_afun(
    a_count_abnormal_by_marked,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )

  lyt <- analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    show_labels = "hidden",
    extra_args = extra_args
  )
  lyt
}

# summarize_glm_count ----------------------------------------------------------
#' Summarize Poisson negative binomial regression
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Summarize results of a Poisson negative binomial regression.
#' This can be used to analyze count and/or frequency data using a linear model.
#' It is specifically useful for analyzing count data (using the Poisson or Negative
#' Binomial distribution) that is result of a generalized linear model of one (e.g. arm) or more
#' covariates.
#'
#' @inheritParams h_glm_count
#' @inheritParams argument_convention
#' @param rate_mean_method (`character(1)`)\cr method used to estimate the mean odds ratio. Defaults to `emmeans`.
#'   see details for more information.
#' @param scale (`numeric(1)`)\cr linear scaling factor for rate and confidence intervals. Defaults to `1`.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("summarize_glm_count"), type = "sh")``
#'
#' @details
#' `summarize_glm_count()` uses `s_glm_count()` to calculate the statistics for the table. This
#' analysis function uses [h_glm_count()] to estimate the GLM with [stats::glm()] for Poisson and Quasi-Poisson
#' distributions or [MASS::glm.nb()] for Negative Binomial distribution. All methods assume a
#' logarithmic link function.
#'
#' At this point, rates and confidence intervals are estimated from the model using
#' either [emmeans::emmeans()] when `rate_mean_method = "emmeans"` or [h_ppmeans()]
#' when `rate_mean_method = "ppmeans"`.
#'
#' If a reference group is specified while building the table with `split_cols_by(ref_group)`,
#' no rate ratio or `p-value` are calculated. Otherwise, we use [emmeans::contrast()] to
#' calculate the rate ratio and `p-value` for the reference group. Values are always estimated
#' with `method = "trt.vs.ctrl"` and `ref` equal to the first `arm` value.
#'
#' @name summarize_glm_count
NULL

#' @describeIn summarize_glm_count Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `summarize_glm_count()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_glm_count()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' anl <- tern_ex_adtte %>% filter(PARAMCD == "TNE")
#' anl$AVAL_f <- as.factor(anl$AVAL)
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM", ref_group = "B: Placebo") %>%
#'   add_colcounts() %>%
#'   analyze_vars(
#'     "AVAL_f",
#'     var_labels = "Number of exacerbations per patient",
#'     .stats = c("count_fraction"),
#'     .formats = c("count_fraction" = "xx (xx.xx%)"),
#'     .labels = c("Number of exacerbations per patient")
#'   ) %>%
#'   summarize_glm_count(
#'     vars = "AVAL",
#'     variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = NULL),
#'     conf_level = 0.95,
#'     distribution = "poisson",
#'     rate_mean_method = "emmeans",
#'     var_labels = "Adjusted (P) exacerbation rate (per year)",
#'     table_names = "adjP",
#'     .stats = c("rate"),
#'     .labels = c(rate = "Rate")
#'   ) %>%
#'   summarize_glm_count(
#'     vars = "AVAL",
#'     variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
#'     conf_level = 0.95,
#'     distribution = "quasipoisson",
#'     rate_mean_method = "ppmeans",
#'     var_labels = "Adjusted (QP) exacerbation rate (per year)",
#'     table_names = "adjQP",
#'     .stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
#'     .labels = c(
#'       rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
#'       rate_ratio_ci = "Rate Ratio CI", pval = "p value"
#'     )
#'   ) %>%
#'   summarize_glm_count(
#'     vars = "AVAL",
#'     variables = list(arm = "ARM", offset = "lgTMATRSK", covariates = c("REGION1")),
#'     conf_level = 0.95,
#'     distribution = "negbin",
#'     rate_mean_method = "emmeans",
#'     var_labels = "Adjusted (NB) exacerbation rate (per year)",
#'     table_names = "adjNB",
#'     .stats = c("rate", "rate_ci", "rate_ratio", "rate_ratio_ci", "pval"),
#'     .labels = c(
#'       rate = "Rate", rate_ci = "Rate CI", rate_ratio = "Rate Ratio",
#'       rate_ratio_ci = "Rate Ratio CI", pval = "p value"
#'     )
#'   )
#'
#' build_table(lyt = lyt, df = anl)
#'
#' @export
summarize_glm_count <- function(lyt,
                                vars,
                                variables,
                                distribution,
                                conf_level,
                                rate_mean_method = c("emmeans", "ppmeans")[1],
                                weights = stats::weights,
                                scale = 1,
                                var_labels,
                                na_str = default_na_str(),
                                nested = TRUE,
                                ...,
                                show_labels = "visible",
                                table_names = vars,
                                .stats = get_stats("summarize_glm_count"),
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = c(
                                  "n" = 0L,
                                  "rate" = 0L,
                                  "rate_ci" = 1L,
                                  "rate_ratio" = 0L,
                                  "rate_ratio_ci" = 1L,
                                  "pval" = 1L
                                )) {
  checkmate::assert_choice(rate_mean_method, c("emmeans", "ppmeans"))

  extra_args <- list(
    variables = variables, distribution = distribution, conf_level = conf_level,
    rate_mean_method = rate_mean_method, weights = weights, scale = scale, ...
  )

  # Selecting parameters following the statistics
  .formats <- get_formats_from_stats(.stats, formats_in = .formats)
  .labels <- get_labels_from_stats(.stats, labels_in = .labels)
  .indent_mods <- get_indents_from_stats(.stats, indents_in = .indent_mods)

  afun <- make_afun(
    s_glm_count,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .null_ref_cells = FALSE
  )

  analyze(
    lyt,
    vars,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' @describeIn summarize_glm_count Statistics function that produces a named list of results
#'   of the investigated Poisson model.
#'
#' @return
#' * `s_glm_count()` returns a named `list` of 5 statistics:
#'   * `n`: Count of complete sample size for the group.
#'   * `rate`: Estimated event rate per follow-up time.
#'   * `rate_ci`: Confidence level for estimated rate per follow-up time.
#'   * `rate_ratio`: Ratio of event rates in each treatment arm to the reference arm.
#'   * `rate_ratio_ci`: Confidence level for the rate ratio.
#'   * `pval`: p-value.
#'
#' @keywords internal
s_glm_count <- function(df,
                        .var,
                        .df_row,
                        variables,
                        .ref_group,
                        .in_ref_col,
                        distribution,
                        conf_level,
                        rate_mean_method,
                        weights,
                        scale = 1) {
  arm <- variables$arm

  y <- df[[.var]]
  smry_level <- as.character(unique(df[[arm]]))

  # ensure there is only 1 value
  checkmate::assert_scalar(smry_level)

  results <- h_glm_count(
    .var = .var,
    .df_row = .df_row,
    variables = variables,
    distribution = distribution,
    weights
  )

  if (rate_mean_method == "emmeans") {
    emmeans_smry <- summary(results$emmeans_fit, level = conf_level)
  } else if (rate_mean_method == "ppmeans") {
    emmeans_smry <- h_ppmeans(results$glm_fit, .df_row, arm, conf_level)
  }

  emmeans_smry_level <- emmeans_smry[emmeans_smry[[arm]] == smry_level, ]

  # This happens if there is a reference col. No Ratio is calculated?
  if (.in_ref_col) {
    list(
      n = length(y[!is.na(y)]),
      rate = formatters::with_label(
        ifelse(distribution == "negbin", emmeans_smry_level$response * scale, emmeans_smry_level$rate * scale),
        "Adjusted Rate"
      ),
      rate_ci = formatters::with_label(
        c(emmeans_smry_level$asymp.LCL * scale, emmeans_smry_level$asymp.UCL * scale),
        f_conf_level(conf_level)
      ),
      rate_ratio = formatters::with_label(character(), "Adjusted Rate Ratio"),
      rate_ratio_ci = formatters::with_label(character(), f_conf_level(conf_level)),
      pval = formatters::with_label(character(), "p-value")
    )
  } else {
    emmeans_contrasts <- emmeans::contrast(
      results$emmeans_fit,
      method = "trt.vs.ctrl",
      ref = grep(
        as.character(unique(.ref_group[[arm]])),
        as.data.frame(results$emmeans_fit)[[arm]]
      )
    )

    contrasts_smry <- summary(
      emmeans_contrasts,
      infer = TRUE,
      adjust = "none"
    )

    smry_contrasts_level <- contrasts_smry[grepl(smry_level, contrasts_smry$contrast), ]

    list(
      n = length(y[!is.na(y)]),
      rate = formatters::with_label(
        ifelse(distribution == "negbin",
          emmeans_smry_level$response * scale,
          emmeans_smry_level$rate * scale
        ),
        "Adjusted Rate"
      ),
      rate_ci = formatters::with_label(
        c(emmeans_smry_level$asymp.LCL * scale, emmeans_smry_level$asymp.UCL * scale),
        f_conf_level(conf_level)
      ),
      rate_ratio = formatters::with_label(
        smry_contrasts_level$ratio,
        "Adjusted Rate Ratio"
      ),
      rate_ratio_ci = formatters::with_label(
        c(smry_contrasts_level$asymp.LCL, smry_contrasts_level$asymp.UCL),
        f_conf_level(conf_level)
      ),
      pval = formatters::with_label(
        smry_contrasts_level$p.value,
        "p-value"
      )
    )
  }
}
# h_glm_count ------------------------------------------------------------------
#' Helper functions for Poisson models
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Helper functions that returns the results of [stats::glm()] when Poisson or Quasi-Poisson
#' distributions are needed (see `family` parameter), or [MASS::glm.nb()] for Negative Binomial
#' distributions. Link function for the GLM is `log`.
#'
#' @inheritParams argument_convention
#'
#' @seealso [summarize_glm_count]
#'
#' @name h_glm_count
NULL

#' @describeIn h_glm_count Helper function to return the results of the
#'   selected model (Poisson, Quasi-Poisson, negative binomial).
#'
#' @param .df_row (`data.frame`)\cr dataset that includes all the variables that are called
#'   in `.var` and `variables`.
#' @param variables (named `list` of `string`)\cr list of additional analysis variables, with
#'   expected elements:
#'   * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple
#'     groups will be summarized. Specifically, the first level of `arm` variable is taken as the
#'     reference group.
#'   * `covariates` (`character`)\cr a vector that can contain single variable names (such as
#'     `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
#'   * `offset` (`numeric`)\cr a numeric vector or scalar adding an offset.
#' @param distribution (`character`)\cr a character value specifying the distribution
#'   used in the regression (Poisson, Quasi-Poisson, negative binomial).
#' @param weights (`character`)\cr a character vector specifying weights used
#'   in averaging predictions. Number of weights must equal the number of levels included in the covariates.
#'   Weights option passed to [emmeans::emmeans()].
#'
#' @return
#' * `h_glm_count()` returns the results of the selected model.
#'
#' @keywords internal
h_glm_count <- function(.var,
                        .df_row,
                        variables,
                        distribution,
                        weights) {
  checkmate::assert_subset(distribution, c("poisson", "quasipoisson", "negbin"), empty.ok = FALSE)
  switch(distribution,
    poisson = h_glm_poisson(.var, .df_row, variables, weights),
    quasipoisson = h_glm_quasipoisson(.var, .df_row, variables, weights),
    negbin = h_glm_negbin(.var, .df_row, variables, weights)
  )
}

#' @describeIn h_glm_count Helper function to return results of a Poisson model.
#'
#' @return
#' * `h_glm_poisson()` returns the results of a Poisson model.
#'
#' @keywords internal
h_glm_poisson <- function(.var,
                          .df_row,
                          variables,
                          weights) {
  arm <- variables$arm
  covariates <- variables$covariates

  formula <- stats::as.formula(paste0(
    .var, " ~ ",
    " + ",
    paste(covariates, collapse = " + "),
    " + ",
    arm
  ))

  if (is.null(variables$offset)) {
    glm_fit <- stats::glm(
      formula = formula,
      data = .df_row,
      family = stats::poisson(link = "log")
    )
  } else {
    offset <- .df_row[[variables$offset]]
    glm_fit <- stats::glm(
      formula = formula,
      offset = offset,
      data = .df_row,
      family = stats::poisson(link = "log")
    )
  }

  emmeans_fit <- emmeans::emmeans(
    glm_fit,
    specs = arm,
    data = .df_row,
    type = "response",
    offset = 0,
    weights = weights
  )

  list(
    glm_fit = glm_fit,
    emmeans_fit = emmeans_fit
  )
}

#' @describeIn h_glm_count Helper function to return results of a Quasi-Poisson model.
#'
#' @return
#' * `h_glm_quasipoisson()` returns the results of a Quasi-Poisson model.
#'
#' @keywords internal
h_glm_quasipoisson <- function(.var,
                               .df_row,
                               variables,
                               weights) {
  arm <- variables$arm
  covariates <- variables$covariates

  formula <- stats::as.formula(paste0(
    .var, " ~ ",
    " + ",
    paste(covariates, collapse = " + "),
    " + ",
    arm
  ))

  if (is.null(variables$offset)) {
    glm_fit <- stats::glm(
      formula = formula,
      data = .df_row,
      family = stats::quasipoisson(link = "log")
    )
  } else {
    offset <- .df_row[[variables$offset]]
    glm_fit <- stats::glm(
      formula = formula,
      offset = offset,
      data = .df_row,
      family = stats::quasipoisson(link = "log")
    )
  }
  emmeans_fit <- emmeans::emmeans(
    glm_fit,
    specs = arm,
    data = .df_row,
    type = "response",
    offset = 0,
    weights = weights
  )

  list(
    glm_fit = glm_fit,
    emmeans_fit = emmeans_fit
  )
}

#' @describeIn h_glm_count Helper function to return results of a negative binomial model.
#'
#' @return
#' * `h_glm_negbin()` returns the results of a negative binomial model.
#'
#' @keywords internal
h_glm_negbin <- function(.var,
                         .df_row,
                         variables,
                         weights) {
  arm <- variables$arm
  covariates <- variables$covariates
  formula <- stats::as.formula(paste0(
    .var, " ~ ",
    " + ",
    paste(covariates, collapse = " + "),
    " + ",
    arm
  ))

  if (is.null(variables$offset)) {
    formula <- stats::as.formula(paste0(
      .var, " ~ ",
      " + ",
      paste(covariates, collapse = " + "),
      " + ",
      arm
    ))
  } else {
    offset <- variables$offset
    formula_txt <- sprintf(
      "%s ~ %s + %s + offset(%s)",
      .var,
      arm, paste0(covariates, collapse = " + "), offset
    )
    formula <- stats::as.formula(
      formula_txt
    )
  }

  glm_fit <- MASS::glm.nb(
    formula = formula,
    data = .df_row,
    link = "log"
  )

  emmeans_fit <- emmeans::emmeans(
    glm_fit,
    specs = arm,
    data = .df_row,
    type = "response",
    offset = 0,
    weights = weights
  )

  list(
    glm_fit = glm_fit,
    emmeans_fit = emmeans_fit
  )
}

# h_ppmeans --------------------------------------------------------------------
#' Function to return the estimated means using predicted probabilities
#'
#' @description
#' For each arm level, the predicted mean rate is calculated using the fitted model object, with `newdata`
#' set to the result of `stats::model.frame`, a reconstructed data or the original data, depending on the
#' object formula (coming from the fit). The confidence interval is derived using the `conf_level` parameter.
#'
#' @param obj (`glm.fit`)\cr fitted model object used to derive the mean rate estimates in each treatment arm.
#' @param .df_row (`data.frame`)\cr dataset that includes all the variables that are called in `.var` and `variables`.
#' @param arm (`string`)\cr group variable, for which the covariate adjusted means of multiple groups will be
#'   summarized. Specifically, the first level of `arm` variable is taken as the reference group.
#' @param conf_level (`proportion`)\cr value used to derive the confidence interval for the rate.
#'
#' @return
#' * `h_ppmeans()` returns the estimated means.
#'
#' @seealso [summarize_glm_count()].
#'
#' @export
h_ppmeans <- function(obj, .df_row, arm, conf_level) {
  alpha <- 1 - conf_level
  p <- 1 - alpha / 2

  arm_levels <- levels(.df_row[[arm]])

  out <- lapply(arm_levels, function(lev) {
    temp <- .df_row
    temp[[arm]] <- factor(lev, levels = arm_levels)

    mf <- stats::model.frame(obj$formula, data = temp)
    X <- stats::model.matrix(obj$formula, data = mf) # nolint

    rate <- stats::predict(obj, newdata = mf, type = "response")
    rate_hat <- mean(rate)

    zz <- colMeans(rate * X)
    se <- sqrt(as.numeric(t(zz) %*% stats::vcov(obj) %*% zz))
    rate_lwr <- rate_hat * exp(-stats::qnorm(p) * se / rate_hat)
    rate_upr <- rate_hat * exp(stats::qnorm(p) * se / rate_hat)

    c(rate_hat, rate_lwr, rate_upr)
  })

  names(out) <- arm_levels
  out <- do.call(rbind, out)
  if ("negbin" %in% class(obj)) {
    colnames(out) <- c("response", "asymp.LCL", "asymp.UCL")
  } else {
    colnames(out) <- c("rate", "asymp.LCL", "asymp.UCL")
  }
  out <- as.data.frame(out)
  out[[arm]] <- rownames(out)
  out
}

#' Count patients by most extreme post-baseline toxicity grade per direction of abnormality
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_abnormal_by_worst_grade()] creates a layout element to count patients by highest (worst)
#' analysis toxicity grade post-baseline for each direction, categorized by parameter value.
#'
#' This function analyzes primary analysis variable `var` which indicates toxicity grades. Additional
#' analysis variables that can be supplied as a list via the `variables` parameter are `id` (defaults to
#' `USUBJID`), a variable to indicate unique subject identifiers, `param` (defaults to `PARAM`), a variable
#' to indicate parameter values, and `grade_dir` (defaults to `GRADE_DIR`), a variable to indicate directions
#' (e.g. High or Low) for each toxicity grade supplied in `var`.
#'
#' For each combination of `param` and `grade_dir` levels, patient counts by worst
#' grade are calculated as follows:
#'   * `1` to `4`: The number of patients with worst grades 1-4, respectively.
#'   * `Any`: The number of patients with at least one abnormality (i.e. grade is not 0).
#'
#' Fractions are calculated by dividing the above counts by the number of patients with at least one
#' valid measurement recorded during treatment.
#'
#' Pre-processing is crucial when using this function and can be done automatically using the
#' [h_adlb_abnormal_by_worst_grade()] helper function. See the description of this function for details on the
#' necessary pre-processing steps.
#'
#' Prior to using this function in your table layout you must use [rtables::split_rows_by()] to create two row
#' splits, one on variable `param` and one on variable `grade_dir`.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("abnormal_by_worst_grade"), type = "sh")``
#'
#' @seealso [h_adlb_abnormal_by_worst_grade()] which pre-processes ADLB data frames to be used in
#'   [count_abnormal_by_worst_grade()].
#'
#' @name abnormal_by_worst_grade
#' @order 1
NULL

#' @describeIn abnormal_by_worst_grade Statistics function which counts patients by worst grade.
#'
#' @return
#' * `s_count_abnormal_by_worst_grade()` returns the single statistic `count_fraction` with grades 1 to 4 and
#'   "Any" results.
#'
#' @keywords internal
s_count_abnormal_by_worst_grade <- function(df, # nolint
                                            .var = "GRADE_ANL",
                                            .spl_context,
                                            variables = list(
                                              id = "USUBJID",
                                              param = "PARAM",
                                              grade_dir = "GRADE_DIR"
                                            )) {
  checkmate::assert_string(.var)
  assert_valid_factor(df[[.var]])
  assert_valid_factor(df[[variables$param]])
  assert_valid_factor(df[[variables$grade_dir]])
  assert_df_with_variables(df, c(a = .var, variables))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))

  # To verify that the `split_rows_by` are performed with correct variables.
  checkmate::assert_subset(c(variables[["param"]], variables[["grade_dir"]]), .spl_context$split)
  first_row <- .spl_context[.spl_context$split == variables[["param"]], ]
  x_lvls <- c(setdiff(levels(df[[.var]]), "0"), "Any")
  result <- split(numeric(0), factor(x_lvls))

  subj <- first_row$full_parent_df[[1]][[variables[["id"]]]]
  subj_cur_col <- subj[first_row$cur_col_subset[[1]]]
  # Some subjects may have a record for high and low directions but
  # should be counted only once.
  denom <- length(unique(subj_cur_col))

  for (lvl in x_lvls) {
    if (lvl != "Any") {
      df_lvl <- df[df[[.var]] == lvl, ]
    } else {
      df_lvl <- df[df[[.var]] != 0, ]
    }
    num <- length(unique(df_lvl[[variables[["id"]]]]))
    fraction <- ifelse(denom == 0, 0, num / denom)
    result[[lvl]] <- formatters::with_label(c(count = num, fraction = fraction), lvl)
  }

  result <- list(count_fraction = result)
  result
}

#' @describeIn abnormal_by_worst_grade Formatted analysis function which is used as `afun`
#'   in `count_abnormal_by_worst_grade()`.
#'
#' @return
#' * `a_count_abnormal_by_worst_grade()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_by_worst_grade <- make_afun( # nolint
  s_count_abnormal_by_worst_grade,
  .formats = c(count_fraction = format_count_fraction)
)

#' @describeIn abnormal_by_worst_grade Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_by_worst_grade()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal_by_worst_grade()` to the table layout.
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#' adlb <- tern_ex_adlb
#'
#' # Data is modified in order to have some parameters with grades only in one direction
#' # and simulate the real data.
#' adlb$ATOXGR[adlb$PARAMCD == "ALT" & adlb$ATOXGR %in% c("1", "2", "3", "4")] <- "-1"
#' adlb$ANRIND[adlb$PARAMCD == "ALT" & adlb$ANRIND == "HIGH"] <- "LOW"
#' adlb$WGRHIFL[adlb$PARAMCD == "ALT"] <- ""
#'
#' adlb$ATOXGR[adlb$PARAMCD == "IGA" & adlb$ATOXGR %in% c("-1", "-2", "-3", "-4")] <- "1"
#' adlb$ANRIND[adlb$PARAMCD == "IGA" & adlb$ANRIND == "LOW"] <- "HIGH"
#' adlb$WGRLOFL[adlb$PARAMCD == "IGA"] <- ""
#'
#' # Pre-processing
#' adlb_f <- adlb %>% h_adlb_abnormal_by_worst_grade()
#'
#' # Map excludes records without abnormal grade since they should not be displayed
#' # in the table.
#' map <- unique(adlb_f[adlb_f$GRADE_DIR != "ZERO", c("PARAM", "GRADE_DIR", "GRADE_ANL")]) %>%
#'   lapply(as.character) %>%
#'   as.data.frame() %>%
#'   arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)
#'
#' basic_table() %>%
#'   split_cols_by("ARMCD") %>%
#'   split_rows_by("PARAM") %>%
#'   split_rows_by("GRADE_DIR", split_fun = trim_levels_to_map(map)) %>%
#'   count_abnormal_by_worst_grade(
#'     var = "GRADE_ANL",
#'     variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR")
#'   ) %>%
#'   build_table(df = adlb_f)
#'
#' @export
#' @order 2
count_abnormal_by_worst_grade <- function(lyt,
                                          var,
                                          variables = list(
                                            id = "USUBJID",
                                            param = "PARAM",
                                            grade_dir = "GRADE_DIR"
                                          ),
                                          na_str = default_na_str(),
                                          nested = TRUE,
                                          ...,
                                          .stats = NULL,
                                          .formats = NULL,
                                          .labels = NULL,
                                          .indent_mods = NULL) {
  extra_args <- list(variables = variables, ...)

  afun <- make_afun(
    a_count_abnormal_by_worst_grade,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )
  analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = "hidden"
  )
}

#' Helper function to prepare ADLB for `count_abnormal_by_worst_grade()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to prepare an ADLB data frame to be used as input in
#' [count_abnormal_by_worst_grade()]. The following pre-processing steps are applied:
#'
#' 1. `adlb` is filtered on variable `avisit` to only include post-baseline visits.
#' 2. `adlb` is filtered on variables `worst_flag_low` and `worst_flag_high` so that only
#'    worst grades (in either direction) are included.
#' 3. From the standard lab grade variable `atoxgr`, the following two variables are derived
#'    and added to `adlb`:
#'   * A grade direction variable (e.g. `GRADE_DIR`). The variable takes value `"HIGH"` when
#'     `atoxgr > 0`, `"LOW"` when `atoxgr < 0`, and `"ZERO"` otherwise.
#'   * A toxicity grade variable (e.g. `GRADE_ANL`) where all negative values from `atoxgr` are
#'     replaced by their absolute values.
#' 4. Unused factor levels are dropped from `adlb` via [droplevels()].
#'
#' @param adlb (`data.frame`)\cr ADLB data frame.
#' @param atoxgr (`string`)\cr name of the analysis toxicity grade variable. This must be a `factor`
#'   variable.
#' @param avisit (`string`)\cr name of the analysis visit variable.
#' @param worst_flag_low (`string`)\cr name of the worst low lab grade flag variable. This variable is
#'   set to `"Y"` when indicating records of worst low lab grades.
#' @param worst_flag_high (`string`)\cr name of the worst high lab grade flag variable. This variable is
#'   set to `"Y"` when indicating records of worst high lab grades.
#'
#' @return `h_adlb_abnormal_by_worst_grade()` returns the `adlb` data frame with two new
#'   variables: `GRADE_DIR` and `GRADE_ANL`.
#'
#' @seealso [abnormal_by_worst_grade]
#'
#' @examples
#' h_adlb_abnormal_by_worst_grade(tern_ex_adlb) %>%
#'   dplyr::select(ATOXGR, GRADE_DIR, GRADE_ANL) %>%
#'   head(10)
#'
#' @export
h_adlb_abnormal_by_worst_grade <- function(adlb,
                                           atoxgr = "ATOXGR",
                                           avisit = "AVISIT",
                                           worst_flag_low = "WGRLOFL",
                                           worst_flag_high = "WGRHIFL") {
  adlb %>%
    dplyr::filter(
      !.data[[avisit]] %in% c("SCREENING", "BASELINE"),
      .data[[worst_flag_low]] == "Y" | .data[[worst_flag_high]] == "Y"
    ) %>%
    dplyr::mutate(
      GRADE_DIR = factor(
        dplyr::case_when(
          .data[[atoxgr]] %in% c("-1", "-2", "-3", "-4") ~ "LOW",
          .data[[atoxgr]] == "0" ~ "ZERO",
          .data[[atoxgr]] %in% c("1", "2", "3", "4") ~ "HIGH"
        ),
        levels = c("LOW", "ZERO", "HIGH")
      ),
      GRADE_ANL = forcats::fct_relevel(
        forcats::fct_recode(.data[[atoxgr]], `1` = "-1", `2` = "-2", `3` = "-3", `4` = "-4"),
        c("0", "1", "2", "3", "4")
      )
    ) %>%
    droplevels()
}

#' Proportion estimation
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [estimate_proportion()] creates a layout element to estimate the proportion of responders
#' within a studied population. The primary analysis variable, `vars`, indicates whether a response has occurred for
#' each record. See the `method` parameter for options of methods to use when constructing the confidence interval of
#' the proportion. Additionally, a stratification variable can be supplied via the `strata` element of the `variables`
#' argument.
#'
#' @inheritParams prop_strat_wilson
#' @inheritParams argument_convention
#' @param method (`string`)\cr the method used to construct the confidence interval
#'   for proportion of successful outcomes; one of `waldcc`, `wald`, `clopper-pearson`,
#'   `wilson`, `wilsonc`, `strat_wilson`, `strat_wilsonc`, `agresti-coull` or `jeffreys`.
#' @param long (`flag`)\cr whether a long description is required.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("estimate_proportion"), type = "sh")``
#'
#' @seealso [h_proportions]
#'
#' @name estimate_proportion
#' @order 1
NULL

#' @describeIn estimate_proportion Statistics function estimating a
#'   proportion along with its confidence interval.
#'
#' @param df (`logical` or `data.frame`)\cr if only a logical vector is used,
#'   it indicates whether each subject is a responder or not. `TRUE` represents
#'   a successful outcome. If a `data.frame` is provided, also the `strata` variable
#'   names must be provided in `variables` as a list element with the strata strings.
#'   In the case of `data.frame`, the logical vector of responses must be indicated as a
#'   variable name in `.var`.
#'
#' @return
#' * `s_proportion()` returns statistics `n_prop` (`n` and proportion) and `prop_ci` (proportion CI) for a
#'   given variable.
#'
#' @examples
#' # Case with only logical vector.
#' rsp_v <- c(1, 0, 1, 0, 1, 1, 0, 0)
#' s_proportion(rsp_v)
#'
#' # Example for Stratified Wilson CI
#' nex <- 100 # Number of example rows
#' dta <- data.frame(
#'   "rsp" = sample(c(TRUE, FALSE), nex, TRUE),
#'   "grp" = sample(c("A", "B"), nex, TRUE),
#'   "f1" = sample(c("a1", "a2"), nex, TRUE),
#'   "f2" = sample(c("x", "y", "z"), nex, TRUE),
#'   stringsAsFactors = TRUE
#' )
#'
#' s_proportion(
#'   df = dta,
#'   .var = "rsp",
#'   variables = list(strata = c("f1", "f2")),
#'   conf_level = 0.90,
#'   method = "strat_wilson"
#' )
#'
#' @export
s_proportion <- function(df,
                         .var,
                         conf_level = 0.95,
                         method = c(
                           "waldcc", "wald", "clopper-pearson",
                           "wilson", "wilsonc", "strat_wilson", "strat_wilsonc",
                           "agresti-coull", "jeffreys"
                         ),
                         weights = NULL,
                         max_iterations = 50,
                         variables = list(strata = NULL),
                         long = FALSE) {
  method <- match.arg(method)
  checkmate::assert_flag(long)
  assert_proportion_value(conf_level)

  if (!is.null(variables$strata)) {
    # Checks for strata
    if (missing(df)) stop("When doing stratified analysis a data.frame with specific columns is needed.")
    strata_colnames <- variables$strata
    checkmate::assert_character(strata_colnames, null.ok = FALSE)
    strata_vars <- stats::setNames(as.list(strata_colnames), strata_colnames)
    assert_df_with_variables(df, strata_vars)

    strata <- interaction(df[strata_colnames])
    strata <- as.factor(strata)

    # Pushing down checks to prop_strat_wilson
  } else if (checkmate::test_subset(method, c("strat_wilson", "strat_wilsonc"))) {
    stop("To use stratified methods you need to specify the strata variables.")
  }
  if (checkmate::test_atomic_vector(df)) {
    rsp <- as.logical(df)
  } else {
    rsp <- as.logical(df[[.var]])
  }
  n <- sum(rsp)
  p_hat <- mean(rsp)

  prop_ci <- switch(method,
    "clopper-pearson" = prop_clopper_pearson(rsp, conf_level),
    "wilson" = prop_wilson(rsp, conf_level),
    "wilsonc" = prop_wilson(rsp, conf_level, correct = TRUE),
    "strat_wilson" = prop_strat_wilson(rsp,
      strata,
      weights,
      conf_level,
      max_iterations,
      correct = FALSE
    )$conf_int,
    "strat_wilsonc" = prop_strat_wilson(rsp,
      strata,
      weights,
      conf_level,
      max_iterations,
      correct = TRUE
    )$conf_int,
    "wald" = prop_wald(rsp, conf_level),
    "waldcc" = prop_wald(rsp, conf_level, correct = TRUE),
    "agresti-coull" = prop_agresti_coull(rsp, conf_level),
    "jeffreys" = prop_jeffreys(rsp, conf_level)
  )

  list(
    "n_prop" = formatters::with_label(c(n, p_hat), "Responders"),
    "prop_ci" = formatters::with_label(
      x = 100 * prop_ci, label = d_proportion(conf_level, method, long = long)
    )
  )
}

#' @describeIn estimate_proportion Formatted analysis function which is used as `afun`
#'   in `estimate_proportion()`.
#'
#' @return
#' * `a_proportion()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @export
a_proportion <- make_afun(
  s_proportion,
  .formats = c(n_prop = "xx (xx.x%)", prop_ci = "(xx.x, xx.x)")
)

#' @describeIn estimate_proportion Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `estimate_proportion()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_proportion()` to the table layout.
#'
#' @examples
#' dta_test <- data.frame(
#'   USUBJID = paste0("S", 1:12),
#'   ARM     = rep(LETTERS[1:3], each = 4),
#'   AVAL    = rep(LETTERS[1:3], each = 4)
#' )
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   estimate_proportion(vars = "AVAL") %>%
#'   build_table(df = dta_test)
#'
#' @export
#' @order 2
estimate_proportion <- function(lyt,
                                vars,
                                conf_level = 0.95,
                                method = c(
                                  "waldcc", "wald", "clopper-pearson",
                                  "wilson", "wilsonc", "strat_wilson", "strat_wilsonc",
                                  "agresti-coull", "jeffreys"
                                ),
                                weights = NULL,
                                max_iterations = 50,
                                variables = list(strata = NULL),
                                long = FALSE,
                                na_str = default_na_str(),
                                nested = TRUE,
                                ...,
                                show_labels = "hidden",
                                table_names = vars,
                                .stats = NULL,
                                .formats = NULL,
                                .labels = NULL,
                                .indent_mods = NULL) {
  extra_args <- list(
    conf_level = conf_level, method = method, weights = weights, max_iterations = max_iterations,
    variables = variables, long = long, ...
  )

  afun <- make_afun(
    a_proportion,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args,
    show_labels = show_labels,
    table_names = table_names
  )
}

#' Helper functions for calculating proportion confidence intervals
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions to calculate different proportion confidence intervals for use in [estimate_proportion()].
#'
#' @inheritParams argument_convention
#' @inheritParams estimate_proportion
#'
#' @return Confidence interval of a proportion.
#'
#' @seealso [estimate_proportion], descriptive function [d_proportion()],
#'  and helper functions [strata_normal_quantile()] and [update_weights_strat_wilson()].
#'
#' @name h_proportions
NULL

#' @describeIn h_proportions Calculates the Wilson interval by calling [stats::prop.test()].
#'  Also referred to as Wilson score interval.
#'
#' @examples
#' rsp <- c(
#'   TRUE, TRUE, TRUE, TRUE, TRUE,
#'   FALSE, FALSE, FALSE, FALSE, FALSE
#' )
#' prop_wilson(rsp, conf_level = 0.9)
#'
#' @export
prop_wilson <- function(rsp, conf_level, correct = FALSE) {
  y <- stats::prop.test(
    sum(rsp),
    length(rsp),
    correct = correct,
    conf.level = conf_level
  )

  as.numeric(y$conf.int)
}

#' @describeIn h_proportions Calculates the stratified Wilson confidence
#'   interval for unequal proportions as described in \insertCite{Yan2010-jt;textual}{tern}
#'
#' @param strata (`factor`)\cr variable with one level per stratum and same length as `rsp`.
#' @param weights (`numeric` or `NULL`)\cr weights for each level of the strata. If `NULL`, they are
#'   estimated using the iterative algorithm proposed in \insertCite{Yan2010-jt;textual}{tern} that
#'   minimizes the weighted squared length of the confidence interval.
#' @param max_iterations (`count`)\cr maximum number of iterations for the iterative procedure used
#'   to find estimates of optimal weights.
#' @param correct (`flag`)\cr whether to include the continuity correction. For further information, see for example
#'   for [stats::prop.test()].
#'
#' @references
#' \insertRef{Yan2010-jt}{tern}
#'
#' @examples
#' # Stratified Wilson confidence interval with unequal probabilities
#'
#' set.seed(1)
#' rsp <- sample(c(TRUE, FALSE), 100, TRUE)
#' strata_data <- data.frame(
#'   "f1" = sample(c("a", "b"), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' )
#' strata <- interaction(strata_data)
#' n_strata <- ncol(table(rsp, strata)) # Number of strata
#'
#' prop_strat_wilson(
#'   rsp = rsp, strata = strata,
#'   conf_level = 0.90
#' )
#'
#' # Not automatic setting of weights
#' prop_strat_wilson(
#'   rsp = rsp, strata = strata,
#'   weights = rep(1 / n_strata, n_strata),
#'   conf_level = 0.90
#' )
#'
#' @export
prop_strat_wilson <- function(rsp,
                              strata,
                              weights = NULL,
                              conf_level = 0.95,
                              max_iterations = NULL,
                              correct = FALSE) {
  checkmate::assert_logical(rsp, any.missing = FALSE)
  checkmate::assert_factor(strata, len = length(rsp))
  assert_proportion_value(conf_level)

  tbl <- table(rsp, strata)
  n_strata <- length(unique(strata))

  # Checking the weights and maximum number of iterations.
  do_iter <- FALSE
  if (is.null(weights)) {
    weights <- rep(1 / n_strata, n_strata) # Initialization for iterative procedure
    do_iter <- TRUE

    # Iteration parameters
    if (is.null(max_iterations)) max_iterations <- 10
    checkmate::assert_int(max_iterations, na.ok = FALSE, null.ok = FALSE, lower = 1)
  }
  checkmate::assert_numeric(weights, lower = 0, upper = 1, any.missing = FALSE, len = n_strata)
  sum_weights <- checkmate::assert_int(sum(weights))
  if (as.integer(sum_weights + 0.5) != 1L) stop("Sum of weights must be 1L.")

  xs <- tbl["TRUE", ]
  ns <- colSums(tbl)
  use_stratum <- (ns > 0)
  ns <- ns[use_stratum]
  xs <- xs[use_stratum]
  ests <- xs / ns
  vars <- ests * (1 - ests) / ns

  strata_qnorm <- strata_normal_quantile(vars, weights, conf_level)

  # Iterative setting of weights if they were not set externally
  weights_new <- if (do_iter) {
    update_weights_strat_wilson(vars, strata_qnorm, weights, ns, max_iterations, conf_level)$weights
  } else {
    weights
  }

  strata_conf_level <- 2 * stats::pnorm(strata_qnorm) - 1

  ci_by_strata <- Map(
    function(x, n) {
      # Classic Wilson's confidence interval
      suppressWarnings(stats::prop.test(x, n, correct = correct, conf.level = strata_conf_level)$conf.int)
    },
    x = xs,
    n = ns
  )
  lower_by_strata <- sapply(ci_by_strata, "[", 1L)
  upper_by_strata <- sapply(ci_by_strata, "[", 2L)

  lower <- sum(weights_new * lower_by_strata)
  upper <- sum(weights_new * upper_by_strata)

  # Return values
  if (do_iter) {
    list(
      conf_int = c(
        lower = lower,
        upper = upper
      ),
      weights = weights_new
    )
  } else {
    list(
      conf_int = c(
        lower = lower,
        upper = upper
      )
    )
  }
}

#' @describeIn h_proportions Calculates the Clopper-Pearson interval by calling [stats::binom.test()].
#'   Also referred to as the `exact` method.
#'
#' @examples
#' prop_clopper_pearson(rsp, conf_level = .95)
#'
#' @export
prop_clopper_pearson <- function(rsp,
                                 conf_level) {
  y <- stats::binom.test(
    x = sum(rsp),
    n = length(rsp),
    conf.level = conf_level
  )
  as.numeric(y$conf.int)
}

#' @describeIn h_proportions Calculates the Wald interval by following the usual textbook definition
#'   for a single proportion confidence interval using the normal approximation.
#'
#' @param correct (`flag`)\cr whether to apply continuity correction.
#'
#' @examples
#' prop_wald(rsp, conf_level = 0.95)
#' prop_wald(rsp, conf_level = 0.95, correct = TRUE)
#'
#' @export
prop_wald <- function(rsp, conf_level, correct = FALSE) {
  n <- length(rsp)
  p_hat <- mean(rsp)
  z <- stats::qnorm((1 + conf_level) / 2)
  q_hat <- 1 - p_hat
  correct <- if (correct) 1 / (2 * n) else 0

  err <- z * sqrt(p_hat * q_hat) / sqrt(n) + correct
  l_ci <- max(0, p_hat - err)
  u_ci <- min(1, p_hat + err)

  c(l_ci, u_ci)
}

#' @describeIn h_proportions Calculates the Agresti-Coull interval. Constructed (for 95% CI) by adding two successes
#'   and two failures to the data and then using the Wald formula to construct a CI.
#'
#' @examples
#' prop_agresti_coull(rsp, conf_level = 0.95)
#'
#' @export
prop_agresti_coull <- function(rsp, conf_level) {
  n <- length(rsp)
  x_sum <- sum(rsp)
  z <- stats::qnorm((1 + conf_level) / 2)

  # Add here both z^2 / 2 successes and failures.
  x_sum_tilde <- x_sum + z^2 / 2
  n_tilde <- n + z^2

  # Then proceed as with the Wald interval.
  p_tilde <- x_sum_tilde / n_tilde
  q_tilde <- 1 - p_tilde
  err <- z * sqrt(p_tilde * q_tilde) / sqrt(n_tilde)
  l_ci <- max(0, p_tilde - err)
  u_ci <- min(1, p_tilde + err)

  c(l_ci, u_ci)
}

#' @describeIn h_proportions Calculates the Jeffreys interval, an equal-tailed interval based on the
#'   non-informative Jeffreys prior for a binomial proportion.
#'
#' @examples
#' prop_jeffreys(rsp, conf_level = 0.95)
#'
#' @export
prop_jeffreys <- function(rsp,
                          conf_level) {
  n <- length(rsp)
  x_sum <- sum(rsp)

  alpha <- 1 - conf_level
  l_ci <- ifelse(
    x_sum == 0,
    0,
    stats::qbeta(alpha / 2, x_sum + 0.5, n - x_sum + 0.5)
  )

  u_ci <- ifelse(
    x_sum == n,
    1,
    stats::qbeta(1 - alpha / 2, x_sum + 0.5, n - x_sum + 0.5)
  )

  c(l_ci, u_ci)
}

#' Description of the proportion summary
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a helper function that describes the analysis in [s_proportion()].
#'
#' @inheritParams s_proportion
#' @param long (`flag`)\cr whether a long or a short (default) description is required.
#'
#' @return String describing the analysis.
#'
#' @export
d_proportion <- function(conf_level,
                         method,
                         long = FALSE) {
  label <- paste0(conf_level * 100, "% CI")

  if (long) label <- paste(label, "for Response Rates")

  method_part <- switch(method,
    "clopper-pearson" = "Clopper-Pearson",
    "waldcc" = "Wald, with correction",
    "wald" = "Wald, without correction",
    "wilson" = "Wilson, without correction",
    "strat_wilson" = "Stratified Wilson, without correction",
    "wilsonc" = "Wilson, with correction",
    "strat_wilsonc" = "Stratified Wilson, with correction",
    "agresti-coull" = "Agresti-Coull",
    "jeffreys" = "Jeffreys",
    stop(paste(method, "does not have a description"))
  )

  paste0(label, " (", method_part, ")")
}

#' Helper function for the estimation of stratified quantiles
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This function wraps the estimation of stratified percentiles when we assume
#' the approximation for large numbers. This is necessary only in the case
#' proportions for each strata are unequal.
#'
#' @inheritParams argument_convention
#' @inheritParams prop_strat_wilson
#'
#' @return Stratified quantile.
#'
#' @seealso [prop_strat_wilson()]
#'
#' @examples
#' strata_data <- table(data.frame(
#'   "f1" = sample(c(TRUE, FALSE), 100, TRUE),
#'   "f2" = sample(c("x", "y", "z"), 100, TRUE),
#'   stringsAsFactors = TRUE
#' ))
#' ns <- colSums(strata_data)
#' ests <- strata_data["TRUE", ] / ns
#' vars <- ests * (1 - ests) / ns
#' weights <- rep(1 / length(ns), length(ns))
#'
#' strata_normal_quantile(vars, weights, 0.95)
#'
#' @export
strata_normal_quantile <- function(vars, weights, conf_level) {
  summands <- weights^2 * vars
  # Stratified quantile
  sqrt(sum(summands)) / sum(sqrt(summands)) * stats::qnorm((1 + conf_level) / 2)
}

#' Helper function for the estimation of weights for `prop_strat_wilson()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This function wraps the iteration procedure that allows you to estimate
#' the weights for each proportional strata. This assumes to minimize the
#' weighted squared length of the confidence interval.
#'
#' @inheritParams prop_strat_wilson
#' @param vars (`numeric`)\cr normalized proportions for each strata.
#' @param strata_qnorm (`numeric(1)`)\cr initial estimation with identical weights of the quantiles.
#' @param initial_weights (`numeric`)\cr initial weights used to calculate `strata_qnorm`. This can
#'   be optimized in the future if we need to estimate better initial weights.
#' @param n_per_strata (`numeric`)\cr number of elements in each strata.
#' @param max_iterations (`integer(1)`)\cr maximum number of iterations to be tried. Convergence is always checked.
#' @param tol (`numeric(1)`)\cr tolerance threshold for convergence.
#'
#' @return A `list` of 3 elements: `n_it`, `weights`, and `diff_v`.
#'
#' @seealso For references and details see [prop_strat_wilson()].
#'
#' @examples
#' vs <- c(0.011, 0.013, 0.012, 0.014, 0.017, 0.018)
#' sq <- 0.674
#' ws <- rep(1 / length(vs), length(vs))
#' ns <- c(22, 18, 17, 17, 14, 12)
#'
#' update_weights_strat_wilson(vs, sq, ws, ns, 100, 0.95, 0.001)
#'
#' @export
update_weights_strat_wilson <- function(vars,
                                        strata_qnorm,
                                        initial_weights,
                                        n_per_strata,
                                        max_iterations = 50,
                                        conf_level = 0.95,
                                        tol = 0.001) {
  it <- 0
  diff_v <- NULL

  while (it < max_iterations) {
    it <- it + 1
    weights_new_t <- (1 + strata_qnorm^2 / n_per_strata)^2
    weights_new_b <- (vars + strata_qnorm^2 / (4 * n_per_strata^2))
    weights_new <- weights_new_t / weights_new_b
    weights_new <- weights_new / sum(weights_new)
    strata_qnorm <- strata_normal_quantile(vars, weights_new, conf_level)
    diff_v <- c(diff_v, sum(abs(weights_new - initial_weights)))
    if (diff_v[length(diff_v)] < tol) break
    initial_weights <- weights_new
  }

  if (it == max_iterations) {
    warning("The heuristic to find weights did not converge with max_iterations = ", max_iterations)
  }

  list(
    "n_it" = it,
    "weights" = weights_new,
    "diff_v" = diff_v
  )
}

#' Count patients with abnormal range values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_abnormal()] creates a layout element to count patients with abnormal analysis range
#' values in each direction.
#'
#' This function analyzes primary analysis variable `var` which indicates abnormal range results.
#' Additional analysis variables that can be supplied as a list via the `variables` parameter are
#' `id` (defaults to `USUBJID`), a variable to indicate unique subject identifiers, and `baseline`
#' (defaults to `BNRIND`), a variable to indicate baseline reference ranges.
#'
#' For each direction specified via the `abnormal` parameter (e.g. High or Low), a fraction of
#' patient counts is returned, with numerator and denominator calculated as follows:
#'   * `num`: The number of patients with this abnormality recorded while on treatment.
#'   * `denom`: The total number of patients with at least one post-baseline assessment.
#'
#' This function assumes that `df` has been filtered to only include post-baseline records.
#'
#' @inheritParams argument_convention
#' @param abnormal (named `list`)\cr list identifying the abnormal range level(s) in `var`. Defaults to
#'   `list(Low = "LOW", High = "HIGH")` but you can also group different levels into the named list,
#'   for example, `abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH"))`.
#' @param exclude_base_abn (`flag`)\cr whether to exclude subjects with baseline abnormality
#'   from numerator and denominator.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("abnormal"), type = "sh")``
#'
#' @note
#' * `count_abnormal()` only considers a single variable that contains multiple abnormal levels.
#' * `df` should be filtered to only include post-baseline records.
#' * The denominator includes patients that may have other abnormal levels at baseline,
#'   and patients missing baseline records. Patients with these abnormalities at
#'   baseline can be optionally excluded from numerator and denominator via the
#'   `exclude_base_abn` parameter.
#'
#' @name abnormal
#' @include formatting_functions.R
#' @order 1
NULL

#' @describeIn abnormal Statistics function which counts patients with abnormal range values
#'   for a single `abnormal` level.
#'
#' @return
#' * `s_count_abnormal()` returns the statistic `fraction` which is a vector with `num` and `denom` counts of patients.
#'
#' @keywords internal
s_count_abnormal <- function(df,
                             .var,
                             abnormal = list(Low = "LOW", High = "HIGH"),
                             variables = list(id = "USUBJID", baseline = "BNRIND"),
                             exclude_base_abn = FALSE) {
  checkmate::assert_list(abnormal, types = "character", names = "named", len = 2, any.missing = FALSE)
  checkmate::assert_true(any(unlist(abnormal) %in% levels(df[[.var]])))
  checkmate::assert_factor(df[[.var]])
  checkmate::assert_flag(exclude_base_abn)
  assert_df_with_variables(df, c(range = .var, variables))
  checkmate::assert_multi_class(df[[variables$baseline]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))

  count_abnormal_single <- function(abn_name, abn) {
    # Patients in the denominator fulfill:
    # - have at least one post-baseline visit
    # - their baseline must not be abnormal if `exclude_base_abn`.
    if (exclude_base_abn) {
      denom_select <- !(df[[variables$baseline]] %in% abn)
    } else {
      denom_select <- TRUE
    }
    denom <- length(unique(df[denom_select, variables$id, drop = TRUE]))

    # Patients in the numerator fulfill:
    # - have at least one post-baseline visit with the required abnormality level
    # - are part of the denominator patients.
    num_select <- (df[[.var]] %in% abn) & denom_select
    num <- length(unique(df[num_select, variables$id, drop = TRUE]))

    formatters::with_label(c(num = num, denom = denom), abn_name)
  }

  # This will define the abnormal levels theoretically possible for a specific lab parameter
  # within a split level of a layout.
  abnormal_lev <- lapply(abnormal, intersect, levels(df[[.var]]))
  abnormal_lev <- abnormal_lev[vapply(abnormal_lev, function(x) length(x) > 0, logical(1))]

  result <- sapply(names(abnormal_lev), function(i) count_abnormal_single(i, abnormal_lev[[i]]), simplify = FALSE)
  result <- list(fraction = result)
  result
}

#' @describeIn abnormal Formatted analysis function which is used as `afun` in `count_abnormal()`.
#'
#' @return
#' * `a_count_abnormal()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal <- make_afun(
  s_count_abnormal,
  .formats = c(fraction = format_fraction)
)

#' @describeIn abnormal Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' df <- data.frame(
#'   USUBJID = as.character(c(1, 1, 2, 2)),
#'   ANRIND = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
#'   BNRIND = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
#'   ONTRTFL = c("", "Y", "", "Y"),
#'   stringsAsFactors = FALSE
#' )
#'
#' # Select only post-baseline records.
#' df <- df %>%
#'   filter(ONTRTFL == "Y")
#'
#' # Layout creating function.
#' basic_table() %>%
#'   count_abnormal(var = "ANRIND", abnormal = list(high = "HIGH", low = "LOW")) %>%
#'   build_table(df)
#'
#' # Passing of statistics function and formatting arguments.
#' df2 <- data.frame(
#'   ID = as.character(c(1, 1, 2, 2)),
#'   RANGE = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
#'   BL_RANGE = factor(c("NORMAL", "NORMAL", "HIGH", "HIGH")),
#'   ONTRTFL = c("", "Y", "", "Y"),
#'   stringsAsFactors = FALSE
#' )
#'
#' # Select only post-baseline records.
#' df2 <- df2 %>%
#'   filter(ONTRTFL == "Y")
#'
#' basic_table() %>%
#'   count_abnormal(
#'     var = "RANGE",
#'     abnormal = list(low = "LOW", high = "HIGH"),
#'     variables = list(id = "ID", baseline = "BL_RANGE")
#'   ) %>%
#'   build_table(df2)
#'
#' @export
#' @order 2
count_abnormal <- function(lyt,
                           var,
                           abnormal = list(Low = "LOW", High = "HIGH"),
                           variables = list(id = "USUBJID", baseline = "BNRIND"),
                           exclude_base_abn = FALSE,
                           na_str = default_na_str(),
                           nested = TRUE,
                           ...,
                           table_names = var,
                           .stats = NULL,
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = NULL) {
  extra_args <- list(abnormal = abnormal, variables = variables, exclude_base_abn = exclude_base_abn, ...)

  afun <- make_afun(
    a_count_abnormal,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "fraction"
  )

  checkmate::assert_string(var)

  analyze(
    lyt = lyt,
    vars = var,
    afun = afun,
    na_str = na_str,
    nested = nested,
    table_names = table_names,
    extra_args = extra_args,
    show_labels = "hidden"
  )
}

#' Estimate proportions of each level of a variable
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze & summarize function [estimate_multinomial_response()] creates a layout element to estimate the
#' proportion and proportion confidence interval for each level of a factor variable. The primary analysis variable,
#' `var`, should be a factor variable, the values of which will be used as labels within the output table.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("estimate_multinomial_response"), type = "sh")``
#'
#' @seealso Relevant description function [d_onco_rsp_label()].
#'
#' @name estimate_multinomial_rsp
#' @order 1
NULL

#' Description of standard oncology response
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Describe the oncology response in a standard way.
#'
#' @param x (`character`)\cr the standard oncology codes to be described.
#'
#' @return Response labels.
#'
#' @seealso [estimate_multinomial_rsp()]
#'
#' @examples
#' d_onco_rsp_label(
#'   c("CR", "PR", "SD", "NON CR/PD", "PD", "NE", "Missing", "<Missing>", "NE/Missing")
#' )
#'
#' # Adding some values not considered in d_onco_rsp_label
#'
#' d_onco_rsp_label(
#'   c("CR", "PR", "hello", "hi")
#' )
#'
#' @export
d_onco_rsp_label <- function(x) {
  x <- as.character(x)
  desc <- c(
    CR           = "Complete Response (CR)",
    PR           = "Partial Response (PR)",
    MR           = "Minimal/Minor Response (MR)",
    MRD          = "Minimal Residual Disease (MRD)",
    SD           = "Stable Disease (SD)",
    PD           = "Progressive Disease (PD)",
    `NON CR/PD`  = "Non-CR or Non-PD (NON CR/PD)",
    NE           = "Not Evaluable (NE)",
    `NE/Missing` = "Missing or unevaluable",
    Missing      = "Missing",
    `NA`         = "Not Applicable (NA)",
    ND           = "Not Done (ND)"
  )

  values_label <- vapply(
    X = x,
    FUN.VALUE = character(1),
    function(val) {
      if (val %in% names(desc)) desc[val] else val
    }
  )

  return(factor(values_label, levels = c(intersect(desc, values_label), setdiff(values_label, desc))))
}

#' @describeIn estimate_multinomial_rsp Statistics function which feeds the length of `x` as number
#'   of successes, and `.N_col` as total number of successes and failures into [s_proportion()].
#'
#' @return
#' * `s_length_proportion()` returns statistics from [s_proportion()].
#'
#' @examples
#' s_length_proportion(rep("CR", 10), .N_col = 100)
#' s_length_proportion(factor(character(0)), .N_col = 100)
#'
#' @export
s_length_proportion <- function(x,
                                .N_col, # nolint
                                ...) {
  checkmate::assert_multi_class(x, classes = c("factor", "character"))
  checkmate::assert_vector(x, min.len = 0, max.len = .N_col)
  checkmate::assert_vector(unique(x), min.len = 0, max.len = 1)

  n_true <- length(x)
  n_false <- .N_col - n_true
  x_logical <- rep(c(TRUE, FALSE), c(n_true, n_false))
  s_proportion(df = x_logical, ...)
}

#' @describeIn estimate_multinomial_rsp Formatted analysis function which is used as `afun`
#'   in `estimate_multinomial_response()`.
#'
#' @return
#' * `a_length_proportion()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_length_proportion(rep("CR", 10), .N_col = 100)
#' a_length_proportion(factor(character(0)), .N_col = 100)
#'
#' @export
a_length_proportion <- make_afun(
  s_length_proportion,
  .formats = c(
    n_prop = "xx (xx.x%)",
    prop_ci = "(xx.xx, xx.xx)"
  )
)

#' @describeIn estimate_multinomial_rsp Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()] and
#'   [rtables::summarize_row_groups()].
#'
#' @return
#' * `estimate_multinomial_response()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_length_proportion()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' # Use of the layout creating function.
#' dta_test <- data.frame(
#'   USUBJID = paste0("S", 1:12),
#'   ARM     = factor(rep(LETTERS[1:3], each = 4)),
#'   AVAL    = c(A = c(1, 1, 1, 1), B = c(0, 0, 1, 1), C = c(0, 0, 0, 0))
#' ) %>% mutate(
#'   AVALC = factor(AVAL,
#'     levels = c(0, 1),
#'     labels = c("Complete Response (CR)", "Partial Response (PR)")
#'   )
#' )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   estimate_multinomial_response(var = "AVALC")
#'
#' tbl <- build_table(lyt, dta_test)
#'
#' tbl
#'
#' @export
#' @order 2
estimate_multinomial_response <- function(lyt,
                                          var,
                                          na_str = default_na_str(),
                                          nested = TRUE,
                                          ...,
                                          show_labels = "hidden",
                                          table_names = var,
                                          .stats = "prop_ci",
                                          .formats = NULL,
                                          .labels = NULL,
                                          .indent_mods = NULL) {
  extra_args <- list(...)

  afun <- make_afun(
    a_length_proportion,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  lyt <- split_rows_by(lyt, var = var)
  lyt <- summarize_row_groups(lyt, na_str = na_str)

  analyze(
    lyt,
    vars = var,
    afun = afun,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Summarize variables in columns
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [summarize_colvars()] uses the statistics function [s_summary()] to analyze variables that are
#' arranged in columns. The variables to analyze should be specified in the table layout via column splits (see
#' [rtables::split_cols_by()] and [rtables::split_cols_by_multivar()]) prior to using [summarize_colvars()].
#'
#' The function is a minimal wrapper for [rtables::analyze_colvars()], a function typically used to apply different
#' analysis methods in rows for each column variable. To use the analysis methods as column labels, please refer to
#' the [analyze_vars_in_cols()] function.
#'
#' @inheritParams argument_convention
#' @param ... arguments passed to [s_summary()].
#' @param .indent_mods (named `vector` of `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @return
#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output
#' in columns, and add it to the table layout.
#'
#' @seealso [rtables::split_cols_by_multivar()] and [`analyze_colvars_functions`].
#'
#' @examples
#' dta_test <- data.frame(
#'   USUBJID = rep(1:6, each = 3),
#'   PARAMCD = rep("lab", 6 * 3),
#'   AVISIT = rep(paste0("V", 1:3), 6),
#'   ARM = rep(LETTERS[1:3], rep(6, 3)),
#'   AVAL = c(9:1, rep(NA, 9)),
#'   CHG = c(1:9, rep(NA, 9))
#' )
#'
#' ## Default output within a `rtables` pipeline.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
#'   summarize_colvars() %>%
#'   build_table(dta_test)
#'
#' ## Selection of statistics, formats and labels also work.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
#'   summarize_colvars(
#'     .stats = c("n", "mean_sd"),
#'     .formats = c("mean_sd" = "xx.x, xx.x"),
#'     .labels = c(n = "n", mean_sd = "Mean, SD")
#'   ) %>%
#'   build_table(dta_test)
#'
#' ## Use arguments interpreted by `s_summary`.
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("AVISIT") %>%
#'   split_cols_by_multivar(vars = c("AVAL", "CHG")) %>%
#'   summarize_colvars(na.rm = FALSE) %>%
#'   build_table(dta_test)
#'
#' @export
summarize_colvars <- function(lyt,
                              ...,
                              na_str = default_na_str(),
                              .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
                              .formats = NULL,
                              .labels = NULL,
                              .indent_mods = NULL) {
  extra_args <- list(.stats = .stats, na_str = na_str, ...)
  if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
  if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
  if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods

  analyze_colvars(
    lyt,
    afun = a_summary,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Count the number of patients with a particular event
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_patients_with_event()] creates a layout element to calculate patient counts for a
#' user-specified set of events.
#'
#' This function analyzes primary analysis variable `vars` which indicates unique subject identifiers. Events
#' are defined by the user as a named vector via the `filters` argument, where each name corresponds to a
#' variable and each value is the value(s) that that variable takes for the event.
#'
#' If there are multiple records with the same event recorded for a patient, only one occurrence is counted.
#'
#' @inheritParams argument_convention
#' @param filters (`character`)\cr a character vector specifying the column names and flag variables
#'   to be used for counting the number of unique identifiers satisfying such conditions.
#'   Multiple column names and flags are accepted in this format
#'   `c("column_name1" = "flag1", "column_name2" = "flag2")`.
#'   Note that only equality is being accepted as condition.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_patients_with_event"), type = "sh")``
#'
#' @seealso [count_patients_with_flags()]
#'
#' @name count_patients_with_event
#' @order 1
NULL

#' @describeIn count_patients_with_event Statistics function which counts the number of patients for which
#'   the defined event has occurred.
#'
#' @inheritParams analyze_variables
#' @param .var (`string`)\cr name of the column that contains the unique identifier.
#'
#' @return
#' * `s_count_patients_with_event()` returns the count and fraction of unique identifiers with the defined event.
#'
#' @examples
#' s_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y"),
#' )
#'
#' s_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL")
#' )
#'
#' s_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
#'   denom = "N_col",
#'   .N_col = 456
#' )
#'
#' @export
s_count_patients_with_event <- function(df,
                                        .var,
                                        filters,
                                        .N_col = ncol(df), # nolint
                                        .N_row = nrow(df), # nolint
                                        denom = c("n", "N_col", "N_row")) {
  col_names <- names(filters)
  filter_values <- filters

  checkmate::assert_subset(col_names, colnames(df))

  temp <- Map(
    function(x, y) which(df[[x]] == y),
    col_names,
    filter_values
  )
  position_satisfy_filters <- Reduce(intersect, temp)
  id_satisfy_filters <- as.character(unique(df[position_satisfy_filters, ][[.var]]))
  result <- s_count_values(
    as.character(unique(df[[.var]])),
    id_satisfy_filters,
    denom = denom,
    .N_col = .N_col,
    .N_row = .N_row
  )
  result
}

#' @describeIn count_patients_with_event Formatted analysis function which is used as `afun`
#'   in `count_patients_with_event()`.
#'
#' @return
#' * `a_count_patients_with_event()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_patients_with_event(
#'   tern_ex_adae,
#'   .var = "SUBJID",
#'   filters = c("TRTEMFL" = "Y"),
#'   .N_col = 100,
#'   .N_row = 100
#' )
#'
#' @export
a_count_patients_with_event <- function(df,
                                        labelstr = "",
                                        filters,
                                        .N_col, # nolint
                                        .N_row, # nolint
                                        denom = c("n", "N_col", "N_row"),
                                        .df_row,
                                        .var = NULL,
                                        .stats = NULL,
                                        .formats = NULL,
                                        .labels = NULL,
                                        .indent_mods = NULL,
                                        na_str = default_na_str()) {
  x_stats <- s_count_patients_with_event(
    df = df, .var = .var, filters = filters, .N_col, .N_row, denom = denom
  )

  if (is.null(unlist(x_stats))) {
    return(NULL)
  }

  # Fill in with formatting defaults if needed
  .stats <- get_stats("count_patients_with_event", stats_in = .stats)
  .formats <- get_formats_from_stats(.stats, .formats)
  .labels <- get_labels_from_stats(.stats, .labels)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods)

  x_stats <- x_stats[.stats]

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = names(.labels),
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls(),
    .format_na_strs = na_str
  )
}

#' @describeIn count_patients_with_event Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_patients_with_event()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_patients_with_event()` to the table layout.
#'
#' @examples
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_values(
#'     "STUDYID",
#'     values = "AB12345",
#'     .stats = "count",
#'     .labels = c(count = "Total AEs")
#'   ) %>%
#'   count_patients_with_event(
#'     "SUBJID",
#'     filters = c("TRTEMFL" = "Y"),
#'     .labels = c(count_fraction = "Total number of patients with at least one adverse event"),
#'     table_names = "tbl_all"
#'   ) %>%
#'   count_patients_with_event(
#'     "SUBJID",
#'     filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
#'     .labels = c(count_fraction = "Total number of patients with fatal AEs"),
#'     table_names = "tbl_fatal"
#'   ) %>%
#'   count_patients_with_event(
#'     "SUBJID",
#'     filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL", "AEREL" = "Y"),
#'     .labels = c(count_fraction = "Total number of patients with related fatal AEs"),
#'     .indent_mods = c(count_fraction = 2L),
#'     table_names = "tbl_rel_fatal"
#'   )
#'
#' build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_patients_with_event <- function(lyt,
                                      vars,
                                      filters,
                                      riskdiff = FALSE,
                                      na_str = default_na_str(),
                                      nested = TRUE,
                                      ...,
                                      table_names = vars,
                                      .stats = "count_fraction",
                                      .formats = list(count_fraction = format_count_fraction_fixed_dp),
                                      .labels = NULL,
                                      .indent_mods = NULL) {
  checkmate::assert_flag(riskdiff)
  extra_args <- list(
    .stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(filters = filters, ...)

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_patients_with_event" = a_count_patients_with_event),
        s_args = s_args
      )
    )
  }

  analyze(
    lyt = lyt,
    vars = vars,
    afun = ifelse(isFALSE(riskdiff), a_count_patients_with_event, afun_riskdiff),
    show_labels = ifelse(length(vars) > 1, "visible", "hidden"),
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Helper function to create a map data frame for `trim_levels_to_map()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to create a map data frame from the input dataset, which can be used as an argument in the
#' `trim_levels_to_map` split function. Based on different method, the map is constructed differently.
#'
#' @inheritParams argument_convention
#' @param abnormal (named `list`)\cr identifying the abnormal range level(s) in `df`. Based on the levels of
#'   abnormality of the input dataset, it can be something like `list(Low = "LOW LOW", High = "HIGH HIGH")` or
#'   `abnormal = list(Low = "LOW", High = "HIGH"))`
#' @param method (`string`)\cr indicates how the returned map will be constructed. Can be `"default"` or `"range"`.
#'
#' @return A map `data.frame`.
#'
#' @note If method is `"default"`, the returned map will only have the abnormal directions that are observed in the
#'   `df`, and records with all normal values will be excluded to avoid error in creating layout. If method is
#'   `"range"`, the returned map will be based on the rule that at least one observation with low range > 0
#'   for low direction and at least one observation with high range is not missing for high direction.
#'
#' @examples
#' adlb <- df_explicit_na(tern_ex_adlb)
#'
#' h_map_for_count_abnormal(
#'   df = adlb,
#'   variables = list(anl = "ANRIND", split_rows = c("LBCAT", "PARAM")),
#'   abnormal = list(low = c("LOW"), high = c("HIGH")),
#'   method = "default",
#'   na_str = "<Missing>"
#' )
#'
#' df <- data.frame(
#'   USUBJID = c(rep("1", 4), rep("2", 4), rep("3", 4)),
#'   AVISIT = c(
#'     rep("WEEK 1", 2),
#'     rep("WEEK 2", 2),
#'     rep("WEEK 1", 2),
#'     rep("WEEK 2", 2),
#'     rep("WEEK 1", 2),
#'     rep("WEEK 2", 2)
#'   ),
#'   PARAM = rep(c("ALT", "CPR"), 6),
#'   ANRIND = c(
#'     "NORMAL", "NORMAL", "LOW",
#'     "HIGH", "LOW", "LOW", "HIGH", "HIGH", rep("NORMAL", 4)
#'   ),
#'   ANRLO = rep(5, 12),
#'   ANRHI = rep(20, 12)
#' )
#' df$ANRIND <- factor(df$ANRIND, levels = c("LOW", "HIGH", "NORMAL"))
#' h_map_for_count_abnormal(
#'   df = df,
#'   variables = list(
#'     anl = "ANRIND",
#'     split_rows = c("PARAM"),
#'     range_low = "ANRLO",
#'     range_high = "ANRHI"
#'   ),
#'   abnormal = list(low = c("LOW"), high = c("HIGH")),
#'   method = "range",
#'   na_str = "<Missing>"
#' )
#'
#' @export
h_map_for_count_abnormal <- function(df,
                                     variables = list(
                                       anl = "ANRIND",
                                       split_rows = c("PARAM"),
                                       range_low = "ANRLO",
                                       range_high = "ANRHI"
                                     ),
                                     abnormal = list(low = c("LOW", "LOW LOW"), high = c("HIGH", "HIGH HIGH")),
                                     method = c("default", "range"),
                                     na_str = "<Missing>") {
  method <- match.arg(method)
  checkmate::assert_subset(c("anl", "split_rows"), names(variables))
  checkmate::assert_false(anyNA(df[variables$split_rows]))
  assert_df_with_variables(df,
    variables = list(anl = variables$anl, split_rows = variables$split_rows),
    na_level = na_str
  )
  assert_df_with_factors(df, list(val = variables$anl))
  assert_valid_factor(df[[variables$anl]], any.missing = FALSE)
  assert_list_of_variables(variables)
  checkmate::assert_list(abnormal, types = "character", len = 2)

  # Drop usued levels from df as they are not supposed to be in the final map
  df <- droplevels(df)

  normal_value <- setdiff(levels(df[[variables$anl]]), unlist(abnormal))

  # Based on the understanding of clinical data, there should only be one level of normal which is "NORMAL"
  checkmate::assert_vector(normal_value, len = 1)

  # Default method will only have what is observed in the df, and records with all normal values will be excluded to
  # avoid error in layout building.
  if (method == "default") {
    df_abnormal <- subset(df, df[[variables$anl]] %in% unlist(abnormal))
    map <- unique(df_abnormal[c(variables$split_rows, variables$anl)])
    map_normal <- unique(subset(map, select = variables$split_rows))
    map_normal[[variables$anl]] <- normal_value
    map <- rbind(map, map_normal)
  } else if (method == "range") {
    # range method follows the rule that at least one observation with ANRLO > 0 for low
    # direction and at least one observation with ANRHI is not missing for high direction.
    checkmate::assert_subset(c("range_low", "range_high"), names(variables))
    checkmate::assert_subset(c("LOW", "HIGH"), toupper(names(abnormal)))

    assert_df_with_variables(df,
      variables = list(
        range_low = variables$range_low,
        range_high = variables$range_high
      )
    )

    # Define low direction of map
    df_low <- subset(df, df[[variables$range_low]] > 0)
    map_low <- unique(df_low[variables$split_rows])
    low_levels <- unname(unlist(abnormal[toupper(names(abnormal)) == "LOW"]))
    low_levels_df <- as.data.frame(low_levels)
    colnames(low_levels_df) <- variables$anl
    low_levels_df <- do.call("rbind", replicate(nrow(map_low), low_levels_df, simplify = FALSE))
    rownames(map_low) <- NULL # Just to avoid strange row index in case upstream functions changed
    map_low <- map_low[rep(seq_len(nrow(map_low)), each = length(low_levels)), , drop = FALSE]
    map_low <- cbind(map_low, low_levels_df)

    # Define high direction of map
    df_high <- subset(df, df[[variables$range_high]] != na_str | !is.na(df[[variables$range_high]]))
    map_high <- unique(df_high[variables$split_rows])
    high_levels <- unname(unlist(abnormal[toupper(names(abnormal)) == "HIGH"]))
    high_levels_df <- as.data.frame(high_levels)
    colnames(high_levels_df) <- variables$anl
    high_levels_df <- do.call("rbind", replicate(nrow(map_high), high_levels_df, simplify = FALSE))
    rownames(map_high) <- NULL
    map_high <- map_high[rep(seq_len(nrow(map_high)), each = length(high_levels)), , drop = FALSE]
    map_high <- cbind(map_high, high_levels_df)

    # Define normal of map
    map_normal <- unique(rbind(map_low, map_high)[variables$split_rows])
    map_normal[variables$anl] <- normal_value

    map <- rbind(map_low, map_high, map_normal)
  }

  # map should be all characters
  map <- data.frame(lapply(map, as.character), stringsAsFactors = FALSE)

  # sort the map final output by split_rows variables
  for (i in rev(seq_len(length(variables$split_rows)))) {
    map <- map[order(map[[i]]), ]
  }
  map
}

# Utility functions to cooperate with {rtables} package

#' Convert table into matrix of strings
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to use mostly within tests. `with_spaces`parameter allows
#' to test not only for content but also indentation and table structure.
#' `print_txt_to_copy` instead facilitate the testing development by returning a well
#' formatted text that needs only to be copied and pasted in the expected output.
#'
#' @inheritParams formatters::toString
#' @param x (`VTableTree`)\cr `rtables` table object.
#' @param with_spaces (`flag`)\cr whether the tested table should keep the indentation and other relevant spaces.
#' @param print_txt_to_copy (`flag`)\cr utility to have a way to copy the input table directly
#'   into the expected variable instead of copying it too manually.
#'
#' @return A `matrix` of `string`s. If `print_txt_to_copy = TRUE` the well formatted printout of the
#'   table will be printed to console, ready to be copied as a expected value.
#'
#' @examples
#' tbl <- basic_table() %>%
#'   split_rows_by("SEX") %>%
#'   split_cols_by("ARM") %>%
#'   analyze("AGE") %>%
#'   build_table(tern_ex_adsl)
#'
#' to_string_matrix(tbl, widths = ceiling(propose_column_widths(tbl) / 2))
#'
#' @export
to_string_matrix <- function(x, widths = NULL, max_width = NULL,
                             hsep = formatters::default_hsep(),
                             with_spaces = TRUE, print_txt_to_copy = FALSE) {
  checkmate::assert_flag(with_spaces)
  checkmate::assert_flag(print_txt_to_copy)
  checkmate::assert_int(max_width, null.ok = TRUE)

  if (inherits(x, "MatrixPrintForm")) {
    tx <- x
  } else {
    tx <- matrix_form(x, TRUE)
  }

  tf_wrap <- FALSE
  if (!is.null(max_width)) {
    tf_wrap <- TRUE
  }

  # Producing the matrix to test
  if (with_spaces) {
    out <- strsplit(toString(tx, widths = widths, tf_wrap = tf_wrap, max_width = max_width, hsep = hsep), "\n")[[1]]
  } else {
    out <- tx$strings
  }

  # Printing to console formatted output that needs to be copied in "expected"
  if (print_txt_to_copy) {
    out_tmp <- out
    if (!with_spaces) {
      out_tmp <- apply(out, 1, paste0, collapse = '", "')
    }
    cat(paste0('c(\n  "', paste0(out_tmp, collapse = '",\n  "'), '"\n)'))
  }

  # Return values
  return(out)
}

#' Blank for missing input
#'
#' Helper function to use in tabulating model results.
#'
#' @param x (`vector`)\cr input for a cell.
#'
#' @return An empty `character` vector if all entries in `x` are missing (`NA`), otherwise
#'   the unlisted version of `x`.
#'
#' @keywords internal
unlist_and_blank_na <- function(x) {
  unl <- unlist(x)
  if (all(is.na(unl))) {
    character()
  } else {
    unl
  }
}

#' Constructor for content functions given a data frame with flag input
#'
#' This can be useful for tabulating model results.
#'
#' @param analysis_var (`string`)\cr variable name for the column containing values to be returned by the
#'   content function.
#' @param flag_var (`string`)\cr variable name for the logical column identifying which row should be returned.
#' @param format (`string`)\cr `rtables` format to use.
#'
#' @return A content function which gives `df$analysis_var` at the row identified by
#'   `.df_row$flag` in the given format.
#'
#' @keywords internal
cfun_by_flag <- function(analysis_var,
                         flag_var,
                         format = "xx",
                         .indent_mods = NULL) {
  checkmate::assert_string(analysis_var)
  checkmate::assert_string(flag_var)
  function(df, labelstr) {
    row_index <- which(df[[flag_var]])
    x <- unlist_and_blank_na(df[[analysis_var]][row_index])
    formatters::with_label(
      rcell(x, format = format, indent_mod = .indent_mods),
      labelstr
    )
  }
}

#' Content row function to add row total to labels
#'
#' This takes the label of the latest row split level and adds the row total from `df` in parentheses.
#' This function differs from [c_label_n_alt()] by taking row counts from `df` rather than
#' `alt_counts_df`, and is used by [add_rowcounts()] when `alt_counts` is set to `FALSE`.
#'
#' @inheritParams argument_convention
#'
#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.
#'
#' @note It is important here to not use `df` but rather `.N_row` in the implementation, because
#'   the former is already split by columns and will refer to the first column of the data only.
#'
#' @seealso [c_label_n_alt()] which performs the same function but retrieves row counts from
#'   `alt_counts_df` instead of `df`.
#'
#' @keywords internal
c_label_n <- function(df,
                      labelstr,
                      .N_row) { # nolint
  label <- paste0(labelstr, " (N=", .N_row, ")")
  in_rows(
    .list = list(row_count = formatters::with_label(c(.N_row, .N_row), label)),
    .formats = c(row_count = function(x, ...) "")
  )
}

#' Content row function to add `alt_counts_df` row total to labels
#'
#' This takes the label of the latest row split level and adds the row total from `alt_counts_df`
#' in parentheses. This function differs from [c_label_n()] by taking row counts from `alt_counts_df`
#' rather than `df`, and is used by [add_rowcounts()] when `alt_counts` is set to `TRUE`.
#'
#' @inheritParams argument_convention
#'
#' @return A list with formatted [rtables::CellValue()] with the row count value and the correct label.
#'
#' @seealso [c_label_n()] which performs the same function but retrieves row counts from `df` instead
#'   of `alt_counts_df`.
#'
#' @keywords internal
c_label_n_alt <- function(df,
                          labelstr,
                          .alt_df_row) {
  N_row_alt <- nrow(.alt_df_row) # nolint
  label <- paste0(labelstr, " (N=", N_row_alt, ")")
  in_rows(
    .list = list(row_count = formatters::with_label(c(N_row_alt, N_row_alt), label)),
    .formats = c(row_count = function(x, ...) "")
  )
}

#' Layout-creating function to add row total counts
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This works analogously to [rtables::add_colcounts()] but on the rows. This function
#'  is a wrapper for [rtables::summarize_row_groups()].
#'
#' @inheritParams argument_convention
#' @param alt_counts (`flag`)\cr whether row counts should be taken from `alt_counts_df` (`TRUE`)
#'   or from `df` (`FALSE`). Defaults to `FALSE`.
#'
#' @return A modified layout where the latest row split labels now have the row-wise
#'   total counts (i.e. without column-based subsetting) attached in parentheses.
#'
#' @note Row count values are contained in these row count rows but are not displayed
#'   so that they are not considered zero rows by default when pruning.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   split_rows_by("RACE", split_fun = drop_split_levels) %>%
#'   add_rowcounts() %>%
#'   analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx") %>%
#'   build_table(DM)
#'
#' @export
add_rowcounts <- function(lyt, alt_counts = FALSE) {
  summarize_row_groups(
    lyt,
    cfun = if (alt_counts) c_label_n_alt else c_label_n
  )
}

#' Obtain column indices
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper function to extract column indices from a `VTableTree` for a given
#' vector of column names.
#'
#' @param table_tree (`VTableTree`)\cr `rtables` table object to extract the indices from.
#' @param col_names (`character`)\cr vector of column names.
#'
#' @return A vector of column indices.
#'
#' @export
h_col_indices <- function(table_tree, col_names) {
  checkmate::assert_class(table_tree, "VTableNodeInfo")
  checkmate::assert_subset(col_names, names(attr(col_info(table_tree), "cextra_args")), empty.ok = FALSE)
  match(col_names, names(attr(col_info(table_tree), "cextra_args")))
}

#' Labels or names of list elements
#'
#' Internal helper function for working with nested statistic function results which typically
#' don't have labels but names that we can use.
#'
#' @param x (`list`)\cr a list.
#'
#' @return A `character` vector with the labels or names for the list elements.
#'
#' @keywords internal
labels_or_names <- function(x) {
  checkmate::assert_multi_class(x, c("data.frame", "list"))
  labs <- sapply(x, obj_label)
  nams <- rlang::names2(x)
  label_is_null <- sapply(labs, is.null)
  result <- unlist(ifelse(label_is_null, nams, labs))
  return(result)
}

#' Convert to `rtable`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is a new generic function to convert objects to `rtable` tables.
#'
#' @param x (`data.frame`)\cr the object which should be converted to an `rtable`.
#' @param ... additional arguments for methods.
#'
#' @return An `rtables` table object. Note that the concrete class will depend on the method used.
#'
#' @export
as.rtable <- function(x, ...) { # nolint
  UseMethod("as.rtable", x)
}

#' @describeIn as.rtable Method for converting a `data.frame` that contains numeric columns to `rtable`.
#'
#' @param format (`string` or `function`)\cr the format which should be used for the columns.
#'
#' @method as.rtable data.frame
#'
#' @examples
#' x <- data.frame(
#'   a = 1:10,
#'   b = rnorm(10)
#' )
#' as.rtable(x)
#'
#' @export
as.rtable.data.frame <- function(x, format = "xx.xx", ...) {
  checkmate::assert_numeric(unlist(x))
  do.call(
    rtable,
    c(
      list(
        header = labels_or_names(x),
        format = format
      ),
      Map(
        function(row, row_name) {
          do.call(
            rrow,
            c(as.list(unname(row)),
              row.name = row_name
            )
          )
        },
        row = as.data.frame(t(x)),
        row_name = rownames(x)
      )
    )
  )
}

#' Split parameters
#'
#' @description `r lifecycle::badge("stable")`
#'
#' It divides the data in the vector `param` into the groups defined by `f` based on specified `values`. It is relevant
#' in `rtables` layers so as to distribute parameters `.stats` or' `.formats` into lists with items corresponding to
#' specific analysis function.
#'
#' @param param (`vector`)\cr the parameter to be split.
#' @param value (`vector`)\cr the value used to split.
#' @param f (`list`)\cr the reference to make the split.
#'
#' @return A named `list` with the same element names as `f`, each containing the elements specified in `.stats`.
#'
#' @examples
#' f <- list(
#'   surv = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci"),
#'   surv_diff = c("rate_diff", "rate_diff_ci", "ztest_pval")
#' )
#'
#' .stats <- c("pt_at_risk", "rate_diff")
#' h_split_param(.stats, .stats, f = f)
#'
#' # $surv
#' # [1] "pt_at_risk"
#' #
#' # $surv_diff
#' # [1] "rate_diff"
#'
#' .formats <- c("pt_at_risk" = "xx", "event_free_rate" = "xxx")
#' h_split_param(.formats, names(.formats), f = f)
#'
#' # $surv
#' # pt_at_risk event_free_rate
#' # "xx"           "xxx"
#' #
#' # $surv_diff
#' # NULL
#'
#' @export
h_split_param <- function(param,
                          value,
                          f) {
  y <- lapply(f, function(x) param[value %in% x])
  lapply(y, function(x) if (length(x) == 0) NULL else x)
}

#' Get selected statistics names
#'
#' Helper function to be used for creating `afun`.
#'
#' @param .stats (`vector` or `NULL`)\cr input to the layout creating function. Note that `NULL` means
#'   in this context that all default statistics should be used.
#' @param all_stats (`character`)\cr all statistics which can be selected here potentially.
#'
#' @return A `character` vector with the selected statistics.
#'
#' @keywords internal
afun_selected_stats <- function(.stats, all_stats) {
  checkmate::assert_character(.stats, null.ok = TRUE)
  checkmate::assert_character(all_stats)
  if (is.null(.stats)) {
    all_stats
  } else {
    intersect(.stats, all_stats)
  }
}

#' Add variable labels to top left corner in table
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Helper layout-creating function to append the variable labels of a given variables vector
#' from a given dataset in the top left corner. If a variable label is not found then the
#' variable name itself is used instead. Multiple variable labels are concatenated with slashes.
#'
#' @inheritParams argument_convention
#' @param vars (`character`)\cr variable names of which the labels are to be looked up in `df`.
#' @param indent (`integer(1)`)\cr non-negative number of nested indent space, default to 0L which means no indent.
#'   1L means two spaces indent, 2L means four spaces indent and so on.
#'
#' @return A modified layout with the new variable label(s) added to the top-left material.
#'
#' @note This is not an optimal implementation of course, since we are using here the data set
#'   itself during the layout creation. When we have a more mature `rtables` implementation then
#'   this will also be improved or not necessary anymore.
#'
#' @examples
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   split_rows_by("SEX") %>%
#'   append_varlabels(DM, "SEX") %>%
#'   analyze("AGE", afun = mean) %>%
#'   append_varlabels(DM, "AGE", indent = 1)
#' build_table(lyt, DM)
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("SEX") %>%
#'   analyze("AGE", afun = mean) %>%
#'   append_varlabels(DM, c("SEX", "AGE"))
#' build_table(lyt, DM)
#'
#' @export
append_varlabels <- function(lyt, df, vars, indent = 0L) {
  if (checkmate::test_flag(indent)) {
    warning("indent argument is now accepting integers. Boolean indent will be converted to integers.")
    indent <- as.integer(indent)
  }

  checkmate::assert_data_frame(df)
  checkmate::assert_character(vars)
  checkmate::assert_count(indent)

  lab <- formatters::var_labels(df[vars], fill = TRUE)
  lab <- paste(lab, collapse = " / ")
  space <- paste(rep(" ", indent * 2), collapse = "")
  lab <- paste0(space, lab)

  append_topleft(lyt, lab)
}

#' Default string replacement for `NA` values
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The default string used to represent `NA` values. This value is used as the default
#' value for the `na_str` argument throughout the `tern` package, and printed in place
#' of `NA` values in output tables. If not specified for each `tern` function by the user
#' via the `na_str` argument, or in the R environment options via [set_default_na_str()],
#' then `NA` is used.
#'
#' @param na_str (`string`)\cr single string value to set in the R environment options as
#'   the default value to replace `NA`s. Use `getOption("tern_default_na_str")` to check the
#'   current value set in the R environment (defaults to `NULL` if not set).
#'
#' @name default_na_str
NULL

#' @describeIn default_na_str Accessor for default `NA` value replacement string.
#'
#' @return
#' * `default_na_str` returns the current value if an R environment option has been set
#'   for `"tern_default_na_str"`, or `NA_character_` otherwise.
#'
#' @examples
#' # Default settings
#' default_na_str()
#' getOption("tern_default_na_str")
#'
#' # Set custom value
#' set_default_na_str("<Missing>")
#'
#' # Settings after value has been set
#' default_na_str()
#' getOption("tern_default_na_str")
#'
#' @export
default_na_str <- function() {
  getOption("tern_default_na_str", default = NA_character_)
}

#' @describeIn default_na_str Setter for default `NA` value replacement string. Sets the
#'   option `"tern_default_na_str"` within the R environment.
#'
#' @return
#' * `set_default_na_str` has no return value.
#'
#' @export
set_default_na_str <- function(na_str) {
  checkmate::assert_character(na_str, len = 1, null.ok = TRUE)
  options("tern_default_na_str" = na_str)
}


#' Utilities to handle extra arguments in analysis functions
#'
#' @description `r lifecycle::badge("stable")`
#' Important additional parameters, useful to modify behavior of analysis and summary
#' functions are listed in [rtables::additional_fun_params]. With these utility functions
#' we can retrieve a curated list of these parameters from the environment, and pass them
#' to the analysis functions with dedicated `...`; notice that the final `s_*` function
#' will get them through argument matching.
#'
#' @param extra_afun_params (`list`)\cr list of additional parameters (`character`) to be
#'   retrieved from the environment. Curated list is present in [rtables::additional_fun_params].
#' @param add_alt_df (`logical`)\cr if `TRUE`, the function will also add `.alt_df` and `.alt_df_row`
#'   parameters.
#'
#' @name util_handling_additional_fun_params
NULL

#' @describeIn util_handling_additional_fun_params Retrieve additional parameters from the environment.
#'
#' @return
#' * `retrieve_extra_afun_params` returns a list of the values of the parameters in the environment.
#'
#' @keywords internal
retrieve_extra_afun_params <- function(extra_afun_params) {
  out <- list()
  for (extra_param in extra_afun_params) {
    out <- c(out, list(get(extra_param, envir = parent.frame())))
  }
  setNames(out, extra_afun_params)
}

#' @describeIn util_handling_additional_fun_params Curated list of additional parameters for
#'   analysis functions. Please check [rtables::additional_fun_params] for precise descriptions.
#'
#' @return
#' * `get_additional_afun_params` returns a list of additional parameters.
#'
#' @keywords internal
get_additional_afun_params <- function(add_alt_df = FALSE) {
  out_list <- list(
    .N_col = integer(),
    .N_total = integer(),
    .N_row = integer(),
    .df_row = data.frame(),
    .var = character(),
    .ref_group = character(),
    .ref_full = vector(mode = "numeric"),
    .in_ref_col = logical(),
    .spl_context = data.frame(),
    .all_col_exprs = vector(mode = "expression"),
    .all_col_counts = vector(mode = "integer")
  )

  if (isTRUE(add_alt_df)) {
    out_list <- c(
      out_list,
      .alt_df_row = data.frame(),
      .alt_df = data.frame()
    )
  }

  out_list
}

#' Helper functions for accessing information from `rtables`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' These are a couple of functions that help with accessing the data in `rtables` objects.
#' Currently these work for occurrence tables, which are defined as having a count as the first
#' element and a fraction as the second element in each cell.
#'
#' @seealso [prune_occurrences] for usage of these functions.
#'
#' @name rtables_access
NULL

#' @describeIn rtables_access Helper function to extract the first values from each content
#'   cell and from specified columns in a `TableRow`. Defaults to all columns.
#'
#' @param table_row (`TableRow`)\cr an analysis row in a occurrence table.
#' @param col_names (`character`)\cr the names of the columns to extract from.
#' @param col_indices (`integer`)\cr the indices of the columns to extract from. If `col_names` are provided,
#'   then these are inferred from the names of `table_row`. Note that this currently only works well with a single
#'   column split.
#'
#' @return
#' * `h_row_first_values()` returns a `vector` of numeric values.
#'
#' @examples
#' tbl <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   split_rows_by("RACE") %>%
#'   analyze("AGE", function(x) {
#'     list(
#'       "mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.x (xx.x)"),
#'       "n" = length(x),
#'       "frac" = rcell(c(0.1, 0.1), format = "xx (xx)")
#'     )
#'   }) %>%
#'   build_table(tern_ex_adsl) %>%
#'   prune_table()
#' tree_row_elem <- collect_leaves(tbl[2, ])[[1]]
#' result <- max(h_row_first_values(tree_row_elem))
#' result
#'
#' @export
h_row_first_values <- function(table_row,
                               col_names = NULL,
                               col_indices = NULL) {
  col_indices <- check_names_indices(table_row, col_names, col_indices)
  checkmate::assert_integerish(col_indices)
  checkmate::assert_subset(col_indices, seq_len(ncol(table_row)))

  # Main values are extracted
  row_vals <- row_values(table_row)[col_indices]

  # Main return
  vapply(row_vals, function(rv) {
    if (is.null(rv)) {
      NA_real_
    } else {
      rv[1L]
    }
  }, FUN.VALUE = numeric(1))
}

#' @describeIn rtables_access Helper function that extracts row values and checks if they are
#'   convertible to integers (`integerish` values).
#'
#' @return
#' * `h_row_counts()` returns a `vector` of numeric values.
#'
#' @examples
#' # Row counts (integer values)
#' # h_row_counts(tree_row_elem) # Fails because there are no integers
#' # Using values with integers
#' tree_row_elem <- collect_leaves(tbl[3, ])[[1]]
#' result <- h_row_counts(tree_row_elem)
#' # result
#'
#' @export
h_row_counts <- function(table_row,
                         col_names = NULL,
                         col_indices = NULL) {
  counts <- h_row_first_values(table_row, col_names, col_indices)
  checkmate::assert_integerish(counts)
  counts
}

#' @describeIn rtables_access Helper function to extract fractions from specified columns in a `TableRow`.
#'   More specifically it extracts the second values from each content cell and checks it is a fraction.
#'
#' @return
#' * `h_row_fractions()` returns a `vector` of proportions.
#'
#' @examples
#' # Row fractions
#' tree_row_elem <- collect_leaves(tbl[4, ])[[1]]
#' h_row_fractions(tree_row_elem)
#'
#' @export
h_row_fractions <- function(table_row,
                            col_names = NULL,
                            col_indices = NULL) {
  col_indices <- check_names_indices(table_row, col_names, col_indices)
  row_vals <- row_values(table_row)[col_indices]
  fractions <- sapply(row_vals, "[", 2L)
  checkmate::assert_numeric(fractions, lower = 0, upper = 1)
  fractions
}

#' @describeIn rtables_access Helper function to extract column counts from specified columns in a table.
#'
#' @param table (`VTableNodeInfo`)\cr an occurrence table or row.
#'
#' @return
#' * `h_col_counts()` returns a `vector` of column counts.
#'
#' @export
h_col_counts <- function(table,
                         col_names = NULL,
                         col_indices = NULL) {
  col_indices <- check_names_indices(table, col_names, col_indices)
  counts <- col_counts(table)[col_indices]
  stats::setNames(counts, col_names)
}

#' @describeIn rtables_access Helper function to get first row of content table of current table.
#'
#' @return
#' * `h_content_first_row()` returns a row from an `rtables` table.
#'
#' @export
h_content_first_row <- function(table) {
  ct <- content_table(table)
  tree_children(ct)[[1]]
}

#' @describeIn rtables_access Helper function which says whether current table is a leaf in the tree.
#'
#' @return
#' * `is_leaf_table()` returns a `logical` value indicating whether current table is a leaf.
#'
#' @keywords internal
is_leaf_table <- function(table) {
  children <- tree_children(table)
  child_classes <- unique(sapply(children, class))
  identical(child_classes, "ElementaryTable")
}

#' @describeIn rtables_access Internal helper function that tests standard inputs for column indices.
#'
#' @return
#' * `check_names_indices` returns column indices.
#'
#' @keywords internal
check_names_indices <- function(table_row,
                                col_names = NULL,
                                col_indices = NULL) {
  if (!is.null(col_names)) {
    if (!is.null(col_indices)) {
      stop(
        "Inserted both col_names and col_indices when selecting row values. ",
        "Please choose one."
      )
    }
    col_indices <- h_col_indices(table_row, col_names)
  }
  if (is.null(col_indices)) {
    ll <- ifelse(is.null(ncol(table_row)), length(table_row), ncol(table_row))
    col_indices <- seq_len(ll)
  }

  return(col_indices)
}

#' Occurrence table sorting
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Functions to score occurrence table subtables and rows which can be used in the
#' sorting of occurrence tables.
#'
#' @name score_occurrences
NULL

#' @describeIn score_occurrences Scoring function which sums the counts across all
#'   columns. It will fail if anything else but counts are used.
#'
#' @inheritParams rtables_access
#'
#' @return
#' * `score_occurrences()` returns the sum of counts across all columns of a table row.
#'
#' @seealso [h_row_first_values()]
#'
#' @examples
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   analyze_num_patients(
#'     vars = "USUBJID",
#'     .stats = c("unique"),
#'     .labels = c("Total number of patients with at least one event")
#'   ) %>%
#'   split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE) %>%
#'   summarize_num_patients(
#'     var = "USUBJID",
#'     .stats = c("unique", "nonunique"),
#'     .labels = c(
#'       "Total number of patients with at least one event",
#'       "Total number of events"
#'     )
#'   ) %>%
#'   count_occurrences(vars = "AEDECOD")
#'
#' tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>%
#'   prune_table()
#'
#' tbl_sorted <- tbl %>%
#'   sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_occurrences)
#'
#' tbl_sorted
#'
#' @export
score_occurrences <- function(table_row) {
  row_counts <- h_row_counts(table_row)
  sum(row_counts)
}

#' @describeIn score_occurrences Scoring functions can be produced by this constructor to only include
#'   specific columns in the scoring. See [h_row_counts()] for further information.
#'
#' @inheritParams has_count_in_cols
#'
#' @return
#' * `score_occurrences_cols()` returns a function that sums counts across all specified columns
#'   of a table row.
#'
#' @seealso [h_row_counts()]
#'
#' @examples
#' score_cols_a_and_b <- score_occurrences_cols(col_names = c("A: Drug X", "B: Placebo"))
#'
#' # Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted.
#' # That would require a second pass of `sort_at_path`.
#' tbl_sorted <- tbl %>%
#'   sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_cols_a_and_b)
#'
#' tbl_sorted
#'
#' @export
score_occurrences_cols <- function(...) {
  function(table_row) {
    row_counts <- h_row_counts(table_row, ...)
    sum(row_counts)
  }
}

#' @describeIn score_occurrences Scoring functions produced by this constructor can be used on
#'   subtables: They sum up all specified column counts in the subtable. This is useful when
#'   there is no available content row summing up these counts.
#'
#' @return
#' * `score_occurrences_subtable()` returns a function that sums counts in each subtable
#'   across all specified columns.
#'
#' @examples
#' score_subtable_all <- score_occurrences_subtable(col_names = names(tbl))
#'
#' # Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That
#' # would require a second pass of `sort_at_path`.
#' tbl_sorted <- tbl %>%
#'   sort_at_path(path = c("AEBODSYS"), scorefun = score_subtable_all, decreasing = FALSE)
#'
#' tbl_sorted
#'
#' @export
score_occurrences_subtable <- function(...) {
  score_table_row <- score_occurrences_cols(...)
  function(table_tree) {
    table_rows <- collect_leaves(table_tree)
    counts <- vapply(table_rows, score_table_row, numeric(1))
    sum(counts)
  }
}

#' @describeIn score_occurrences Produces a score function for sorting table by summing the first content row in
#'   specified columns. Note that this is extending [rtables::cont_n_onecol()] and [rtables::cont_n_allcols()].
#'
#' @return
#' * `score_occurrences_cont_cols()` returns a function that sums counts in the first content row in
#'   specified columns.
#'
#' @export
score_occurrences_cont_cols <- function(...) {
  score_table_row <- score_occurrences_cols(...)
  function(table_tree) {
    if (inherits(table_tree, "ContentRow")) {
      return(NA)
    }
    content_row <- h_content_first_row(table_tree)
    score_table_row(content_row)
  }
}

#' Bland-Altman analysis
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Statistics function that uses the Bland-Altman method to assess the agreement between two numerical vectors
#' and calculates a variety of statistics.
#'
#' @inheritParams argument_convention
#' @param y (`numeric`)\cr vector of numbers we want to analyze, to be compared with `x`.
#'
#' @return
#' A named list of the following elements:
#'   * `df`
#'   * `difference_mean`
#'   * `ci_mean`
#'   * `difference_sd`
#'   * `difference_se`
#'   * `upper_agreement_limit`
#'   * `lower_agreement_limit`
#'   * `agreement_limit_se`
#'   * `upper_agreement_limit_ci`
#'   * `lower_agreement_limit_ci`
#'   * `t_value`
#'   * `n`
#'
#' @examples
#' x <- seq(1, 60, 5)
#' y <- seq(5, 50, 4)
#'
#' s_bland_altman(x, y, conf_level = 0.9)
#'
#' @export
s_bland_altman <- function(x, y, conf_level = 0.95) {
  checkmate::assert_numeric(x, min.len = 1, any.missing = TRUE)
  checkmate::assert_numeric(y, len = length(x), any.missing = TRUE)
  checkmate::assert_numeric(conf_level, lower = 0, upper = 1, any.missing = TRUE)

  alpha <- 1 - conf_level

  ind <- complete.cases(x, y) # use only pairwise complete observations, and check if x and y have the same length
  x <- x[ind]
  y <- y[ind]
  n <- sum(ind) # number of 'observations'

  if (n == 0) {
    stop("there is no valid paired data")
  }

  difference <- x - y # vector of differences
  average <- (x + y) / 2 # vector of means
  difference_mean <- mean(difference) # mean difference
  difference_sd <- sd(difference) # SD of differences
  al <- qnorm(1 - alpha / 2) * difference_sd
  upper_agreement_limit <- difference_mean + al # agreement limits
  lower_agreement_limit <- difference_mean - al

  difference_se <- difference_sd / sqrt(n) # standard error of the mean
  al_se <- difference_sd * sqrt(3) / sqrt(n) # standard error of the agreement limit
  tvalue <- qt(1 - alpha / 2, n - 1) # t value for 95% CI calculation
  difference_mean_ci <- difference_se * tvalue
  al_ci <- al_se * tvalue
  upper_agreement_limit_ci <- c(upper_agreement_limit - al_ci, upper_agreement_limit + al_ci)
  lower_agreement_limit_ci <- c(lower_agreement_limit - al_ci, lower_agreement_limit + al_ci)

  list(
    df = data.frame(average, difference),
    difference_mean = difference_mean,
    ci_mean = difference_mean + c(-1, 1) * difference_mean_ci,
    difference_sd = difference_sd,
    difference_se = difference_se,
    upper_agreement_limit = upper_agreement_limit,
    lower_agreement_limit = lower_agreement_limit,
    agreement_limit_se = al_se,
    upper_agreement_limit_ci = upper_agreement_limit_ci,
    lower_agreement_limit_ci = lower_agreement_limit_ci,
    t_value = tvalue,
    n = n
  )
}

#' Bland-Altman plot
#'
#' @description `r lifecycle::badge("experimental")`
#'
#' Graphing function that produces a Bland-Altman plot.
#'
#' @inheritParams s_bland_altman
#'
#' @return A `ggplot` Bland-Altman plot.
#'
#' @examples
#' x <- seq(1, 60, 5)
#' y <- seq(5, 50, 4)
#'
#' g_bland_altman(x = x, y = y, conf_level = 0.9)
#'
#' @export
#' @aliases bland_altman
g_bland_altman <- function(x, y, conf_level = 0.95) {
  result_tem <- s_bland_altman(x, y, conf_level = conf_level)
  xpos <- max(result_tem$df$average) * 0.9 + min(result_tem$df$average) * 0.1
  yrange <- diff(range(result_tem$df$difference))

  p <- ggplot(result_tem$df) +
    geom_point(aes(x = average, y = difference), color = "blue") +
    geom_hline(yintercept = result_tem$difference_mean, color = "blue", linetype = 1) +
    geom_hline(yintercept = 0, color = "blue", linetype = 2) +
    geom_hline(yintercept = result_tem$lower_agreement_limit, color = "red", linetype = 2) +
    geom_hline(yintercept = result_tem$upper_agreement_limit, color = "red", linetype = 2) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$lower_agreement_limit + 0.03 * yrange,
      label = "lower limits of agreement",
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$upper_agreement_limit + 0.03 * yrange,
      label = "upper limits of agreement",
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$difference_mean + 0.03 * yrange,
      label = "mean of difference between two measures",
      color = "blue"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$lower_agreement_limit - 0.03 * yrange,
      label = sprintf("%.2f", result_tem$lower_agreement_limit),
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$upper_agreement_limit - 0.03 * yrange,
      label = sprintf("%.2f", result_tem$upper_agreement_limit),
      color = "red"
    ) +
    annotate(
      "text",
      x = xpos,
      y = result_tem$difference_mean - 0.03 * yrange,
      label = sprintf("%.2f", result_tem$difference_meanm),
      color = "blue"
    ) +
    xlab("Average of two measures") +
    ylab("Difference between two measures")

  return(p)
}

#' Apply 1/3 or 1/2 imputation rule to data
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @param x_stats (named `list`)\cr a named list of statistics, typically the results of [s_summary()].
#' @param stat (`string`)\cr statistic to return the value/NA level of according to the imputation
#'   rule applied.
#' @param imp_rule (`string`)\cr imputation rule setting. Set to `"1/3"` to implement 1/3 imputation
#'   rule or `"1/2"` to implement 1/2 imputation rule.
#' @param post (`flag`)\cr whether the data corresponds to a post-dose time-point (defaults to `FALSE`).
#'   This parameter is only used when `imp_rule` is set to `"1/3"`.
#' @param avalcat_var (`string`)\cr name of variable that indicates whether a row in `df` corresponds
#'   to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of the above
#'   (defaults to `"AVALCAT1"`). Variable `avalcat_var` must be present in `df`.
#'
#' @return A `list` containing statistic value (`val`) and NA level (`na_str`) that should be displayed
#'   according to the specified imputation rule.
#'
#' @seealso [analyze_vars_in_cols()] where this function can be implemented by setting the `imp_rule`
#'   argument.
#'
#' @examples
#' set.seed(1)
#' df <- data.frame(
#'   AVAL = runif(50, 0, 1),
#'   AVALCAT1 = sample(c(1, "BLQ"), 50, replace = TRUE)
#' )
#' x_stats <- s_summary(df$AVAL)
#' imputation_rule(df, x_stats, "max", "1/3")
#' imputation_rule(df, x_stats, "geom_mean", "1/3")
#' imputation_rule(df, x_stats, "mean", "1/2")
#'
#' @export
imputation_rule <- function(df, x_stats, stat, imp_rule, post = FALSE, avalcat_var = "AVALCAT1") {
  checkmate::assert_choice(avalcat_var, names(df))
  checkmate::assert_choice(imp_rule, c("1/3", "1/2"))
  n_blq <- sum(grepl("BLQ|LTR|<[1-9]|<PCLLOQ", df[[avalcat_var]]))
  ltr_blq_ratio <- n_blq / max(1, nrow(df))

  # defaults
  val <- x_stats[[stat]]
  na_str <- "NE"

  if (imp_rule == "1/3") {
    if (!post && stat == "geom_mean") val <- NA # 1/3_pre_LT, 1/3_pre_GT
    if (ltr_blq_ratio > 1 / 3) {
      if (stat != "geom_mean") na_str <- "ND" # 1/3_pre_GT, 1/3_post_GT
      if (!post && !stat %in% c("median", "max")) val <- NA # 1/3_pre_GT
      if (post && !stat %in% c("median", "max", "geom_mean")) val <- NA # 1/3_post_GT
    }
  } else if (imp_rule == "1/2") {
    if (ltr_blq_ratio > 1 / 2 && !stat == "max") {
      val <- NA # 1/2_GT
      na_str <- "ND" # 1/2_GT
    }
  }

  list(val = val, na_str = na_str)
}

#' Add titles, footnotes, page Number, and a bounding box to a grid grob
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This function is useful to label grid grobs (also `ggplot2`, and `lattice` plots)
#' with title, footnote, and page numbers.
#'
#' @inheritParams grid::grob
#' @param grob (`grob`)\cr a grid grob object, optionally `NULL` if only a `grob` with the decoration should be shown.
#' @param titles (`character`)\cr titles given as a vector of strings that are each separated by a newline and wrapped
#'   according to the page width.
#' @param footnotes (`character`)\cr footnotes. Uses the same formatting rules as `titles`.
#' @param page (`string` or `NULL`)\cr page numeration. If `NULL` then no page number is displayed.
#' @param width_titles (`grid::unit`)\cr width of titles. Usually defined as all the available space
#'   `grid::unit(1, "npc")`, it is affected by the parameter `outer_margins`. Right margins (`outer_margins[4]`)
#'   need to be subtracted to the allowed width.
#' @param width_footnotes (`grid::unit`)\cr width of footnotes. Same default and margin correction as `width_titles`.
#' @param border (`flag`)\cr whether a border should be drawn around the plot or not.
#' @param padding (`grid::unit`)\cr padding. A unit object of length 4. Innermost margin between the plot (`grob`)
#'   and, possibly, the border of the plot. Usually expressed in 4 identical values (usually `"lines"`). It defaults
#'   to `grid::unit(rep(1, 4), "lines")`.
#' @param margins (`grid::unit`)\cr margins. A unit object of length 4. Margins between the plot and the other
#'   elements in the list (e.g. titles, plot, and footers). This is usually expressed in 4 `"lines"`, where the
#'   lateral ones are 0s, while top and bottom are 1s. It defaults to `grid::unit(c(1, 0, 1, 0), "lines")`.
#' @param outer_margins (`grid::unit`)\cr outer margins. A unit object of length 4. It defines the general margin of
#'   the plot, considering also decorations like titles, footnotes, and page numbers. It defaults to
#'   `grid::unit(c(2, 1.5, 3, 1.5), "cm")`.
#' @param gp_titles (`gpar`)\cr a `gpar` object. Mainly used to set different `"fontsize"`.
#' @param gp_footnotes (`gpar`)\cr a `gpar` object. Mainly used to set different `"fontsize"`.
#'
#' @return A grid grob (`gTree`).
#'
#' @details The titles and footnotes will be ragged, i.e. each title will be wrapped individually.
#'
#' @examples
#' library(grid)
#'
#' titles <- c(
#'   "Edgar Anderson's Iris Data",
#'   paste(
#'     "This famous (Fisher's or Anderson's) iris data set gives the measurements",
#'     "in centimeters of the variables sepal length and width and petal length",
#'     "and width, respectively, for 50 flowers from each of 3 species of iris."
#'   )
#' )
#'
#' footnotes <- c(
#'   "The species are Iris setosa, versicolor, and virginica.",
#'   paste(
#'     "iris is a data frame with 150 cases (rows) and 5 variables (columns) named",
#'     "Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, and Species."
#'   )
#' )
#'
#' ## empty plot
#' grid.newpage()
#'
#' grid.draw(
#'   decorate_grob(
#'     NULL,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 4 of 10"
#'   )
#' )
#'
#' # grid
#' p <- gTree(
#'   children = gList(
#'     rectGrob(),
#'     xaxisGrob(),
#'     yaxisGrob(),
#'     textGrob("Sepal.Length", y = unit(-4, "lines")),
#'     textGrob("Petal.Length", x = unit(-3.5, "lines"), rot = 90),
#'     pointsGrob(iris$Sepal.Length, iris$Petal.Length, gp = gpar(col = iris$Species), pch = 16)
#'   ),
#'   vp = vpStack(plotViewport(), dataViewport(xData = iris$Sepal.Length, yData = iris$Petal.Length))
#' )
#' grid.newpage()
#' grid.draw(p)
#'
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     grob = p,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 6 of 129"
#'   )
#' )
#'
#' ## with ggplot2
#' library(ggplot2)
#'
#' p_gg <- ggplot2::ggplot(iris, aes(Sepal.Length, Sepal.Width, col = Species)) +
#'   ggplot2::geom_point()
#' p_gg
#' p <- ggplotGrob(p_gg)
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     grob = p,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 6 of 129"
#'   )
#' )
#'
#' ## with lattice
#' library(lattice)
#'
#' xyplot(Sepal.Length ~ Petal.Length, data = iris, col = iris$Species)
#' p <- grid.grab()
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     grob = p,
#'     titles = titles,
#'     footnotes = footnotes,
#'     page = "Page 6 of 129"
#'   )
#' )
#'
#' # with gridExtra - no borders
#' library(gridExtra)
#' grid.newpage()
#' grid.draw(
#'   decorate_grob(
#'     tableGrob(
#'       head(mtcars)
#'     ),
#'     titles = "title",
#'     footnotes = "footnote",
#'     border = FALSE
#'   )
#' )
#'
#' @export
decorate_grob <- function(grob,
                          titles,
                          footnotes,
                          page = "",
                          width_titles = grid::unit(1, "npc"),
                          width_footnotes = grid::unit(1, "npc"),
                          border = TRUE,
                          padding = grid::unit(rep(1, 4), "lines"),
                          margins = grid::unit(c(1, 0, 1, 0), "lines"),
                          outer_margins = grid::unit(c(2, 1.5, 3, 1.5), "cm"),
                          gp_titles = grid::gpar(),
                          gp_footnotes = grid::gpar(fontsize = 8),
                          name = NULL,
                          gp = grid::gpar(),
                          vp = NULL) {
  # External margins need to be taken into account when defining the width of titles and footers
  # because the text is split in advance depending on only the width of the viewport.
  if (any(as.numeric(outer_margins) > 0)) {
    width_titles <- width_titles - outer_margins[4]
    width_footnotes <- width_footnotes - outer_margins[4]
  }

  st_titles <- split_text_grob(
    titles,
    x = 0, y = 1,
    just = c("left", "top"),
    width = width_titles,
    vp = grid::viewport(layout.pos.row = 1, layout.pos.col = 1),
    gp = gp_titles
  )

  st_footnotes <- split_text_grob(
    footnotes,
    x = 0, y = 1,
    just = c("left", "top"),
    width = width_footnotes,
    vp = grid::viewport(layout.pos.row = 3, layout.pos.col = 1),
    gp = gp_footnotes
  )

  pg_footnote <- grid::textGrob(
    paste("\n", page),
    x = 1, y = 0,
    just = c("right", "bottom"),
    vp = grid::viewport(layout.pos.row = 4, layout.pos.col = 1),
    gp = gp_footnotes
  )

  # Initial decoration of the grob -> border, paddings, and margins are used here
  main_plot <- grid::gTree(
    children = grid::gList(
      if (border) grid::rectGrob(),
      grid::gTree(
        children = grid::gList(
          grob
        ),
        vp = grid::plotViewport(margins = padding) # innermost margins of the grob plot
      )
    ),
    vp = grid::vpStack(
      grid::viewport(layout.pos.row = 2, layout.pos.col = 1),
      grid::plotViewport(margins = margins) # margins around the border plot
    )
  )

  grid::gTree(
    grob = grob,
    titles = titles,
    footnotes = footnotes,
    page = page,
    width_titles = width_titles,
    width_footnotes = width_footnotes,
    outer_margins = outer_margins,
    gp_titles = gp_titles,
    gp_footnotes = gp_footnotes,
    children = grid::gList(
      grid::gTree(
        children = grid::gList(
          st_titles,
          main_plot, # main plot with border, padding, and margins
          st_footnotes,
          pg_footnote
        ),
        childrenvp = NULL,
        name = "titles_grob_footnotes",
        vp = grid::vpStack(
          grid::plotViewport(margins = outer_margins), # Main external margins
          grid::viewport(
            layout = grid::grid.layout(
              nrow = 4, ncol = 1,
              heights = grid::unit.c(
                grid::grobHeight(st_titles),
                grid::unit(1, "null"),
                grid::grobHeight(st_footnotes),
                grid::grobHeight(pg_footnote)
              )
            )
          )
        )
      )
    ),
    name = name,
    gp = gp,
    vp = vp,
    cl = "decoratedGrob"
  )
}

# nocov start
#' @importFrom grid validDetails
#' @noRd
validDetails.decoratedGrob <- function(x) {
  checkmate::assert_character(x$titles)
  checkmate::assert_character(x$footnotes)

  if (!is.null(x$grob)) {
    checkmate::assert_true(grid::is.grob(x$grob))
  }
  if (length(x$page) == 1) {
    checkmate::assert_character(x$page)
  }
  if (!grid::is.unit(x$outer_margins)) {
    checkmate::assert_vector(x$outer_margins, len = 4)
  }
  if (!grid::is.unit(x$margins)) {
    checkmate::assert_vector(x$margins, len = 4)
  }
  if (!grid::is.unit(x$padding)) {
    checkmate::assert_vector(x$padding, len = 4)
  }

  x
}

#' @importFrom grid widthDetails
#' @noRd
widthDetails.decoratedGrob <- function(x) {
  grid::unit(1, "null")
}

#' @importFrom grid heightDetails
#' @noRd
heightDetails.decoratedGrob <- function(x) {
  grid::unit(1, "null")
}

#' Split text according to available text width
#'
#' Dynamically wrap text.
#'
#' @inheritParams grid::grid.text
#' @param text (`string`)\cr the text to wrap.
#' @param width (`grid::unit`)\cr a unit object specifying maximum width of text.
#'
#' @return A text `grob`.
#'
#' @details This code is taken from `R Graphics by Paul Murell, 2nd edition`
#'
#' @keywords internal
split_text_grob <- function(text,
                            x = grid::unit(0.5, "npc"),
                            y = grid::unit(0.5, "npc"),
                            width = grid::unit(1, "npc"),
                            just = "centre",
                            hjust = NULL,
                            vjust = NULL,
                            default.units = "npc", # nolint
                            name = NULL,
                            gp = grid::gpar(),
                            vp = NULL) {
  text <- gsub("\\\\n", "\n", text) # fixing cases of mixed behavior (\n and \\n)

  if (!grid::is.unit(x)) x <- grid::unit(x, default.units)
  if (!grid::is.unit(y)) y <- grid::unit(y, default.units)
  if (!grid::is.unit(width)) width <- grid::unit(width, default.units)
  if (grid::unitType(x) %in% c("sum", "min", "max")) x <- grid::convertUnit(x, default.units)
  if (grid::unitType(y) %in% c("sum", "min", "max")) y <- grid::convertUnit(y, default.units)
  if (grid::unitType(width) %in% c("sum", "min", "max")) width <- grid::convertUnit(width, default.units)

  if (length(gp) > 0) { # account for effect of gp on text width -> it was bugging when text was empty
    horizontal_npc_width_no_gp <- grid::convertWidth(
      grid::grobWidth(
        grid::textGrob(
          paste0(text, collapse = "\n")
        )
      ), "npc",
      valueOnly = TRUE
    )
    horizontal_npc_width_with_gp <- grid::convertWidth(grid::grobWidth(
      grid::textGrob(
        paste0(text, collapse = "\n"),
        gp = gp
      )
    ), "npc", valueOnly = TRUE)

    # Adapting width to the input gpar (it is normalized so does not matter what is text)
    width <- width * horizontal_npc_width_no_gp / horizontal_npc_width_with_gp
  }

  ## if it is a fixed unit then we do not need to recalculate when viewport resized
  if (!inherits(width, "unit.arithmetic") && !is.null(attr(width, "unit")) &&
    attr(width, "unit") %in% c("cm", "inches", "mm", "points", "picas", "bigpts", "dida", "cicero", "scaledpts")) { # nolint
    attr(text, "fixed_text") <- paste(vapply(text, split_string, character(1), width = width), collapse = "\n")
  }

  # Fix for split_string in case of residual \n (otherwise is counted as character)
  text2 <- unlist(
    strsplit(
      paste0(text, collapse = "\n"), # for "" cases
      "\n"
    )
  )

  # Final grid text with cat-friendly split_string
  grid::grid.text(
    label = split_string(text2, width),
    x = x, y = y,
    just = just,
    hjust = hjust,
    vjust = vjust,
    rot = 0,
    check.overlap = FALSE,
    name = name,
    gp = gp,
    vp = vp,
    draw = FALSE
  )
}

#' @importFrom grid validDetails
#' @noRd
validDetails.dynamicSplitText <- function(x) {
  checkmate::assert_character(x$text)
  checkmate::assert_true(grid::is.unit(x$width))
  checkmate::assert_vector(x$width, len = 1)
  x
}

#' @importFrom grid heightDetails
#' @noRd
heightDetails.dynamicSplitText <- function(x) {
  txt <- if (!is.null(attr(x$text, "fixed_text"))) {
    attr(x$text, "fixed_text")
  } else {
    paste(vapply(x$text, split_string, character(1), width = x$width), collapse = "\n")
  }
  grid::stringHeight(txt)
}

#' @importFrom grid widthDetails
#' @noRd
widthDetails.dynamicSplitText <- function(x) {
  x$width
}

#' @importFrom grid drawDetails
#' @noRd
drawDetails.dynamicSplitText <- function(x, recording) {
  txt <- if (!is.null(attr(x$text, "fixed_text"))) {
    attr(x$text, "fixed_text")
  } else {
    paste(vapply(x$text, split_string, character(1), width = x$width), collapse = "\n")
  }

  x$width <- NULL
  x$label <- txt
  x$text <- NULL
  class(x) <- c("text", class(x)[-1])

  grid::grid.draw(x)
}
# nocov end

# Adapted from Paul Murell R Graphics 2nd Edition
# https://www.stat.auckland.ac.nz/~paul/RG2e/interactgrid-splittext.R
split_string <- function(text, width) {
  strings <- strsplit(text, " ")
  out_string <- NA
  for (string_i in seq_along(strings)) {
    newline_str <- strings[[string_i]]
    if (length(newline_str) == 0) newline_str <- ""
    if (is.na(out_string[string_i])) {
      out_string[string_i] <- newline_str[[1]][[1]]
      linewidth <- grid::stringWidth(out_string[string_i])
    }
    gapwidth <- grid::stringWidth(" ")
    availwidth <- as.numeric(width)
    if (length(newline_str) > 1) {
      for (i in seq(2, length(newline_str))) {
        width_i <- grid::stringWidth(newline_str[i])
        # Main conversion of allowed text width -> npc units are 0<npc<1. External viewport is used for conversion
        if (grid::convertWidth(linewidth + gapwidth + width_i, grid::unitType(width), valueOnly = TRUE) < availwidth) {
          sep <- " "
          linewidth <- linewidth + gapwidth + width_i
        } else {
          sep <- "\n"
          linewidth <- width_i
        }
        out_string[string_i] <- paste(out_string[string_i], newline_str[i], sep = sep)
      }
    }
  }
  paste(out_string, collapse = "\n")
}

#' Update page number
#'
#' Automatically updates page number.
#'
#' @param npages (`numeric(1)`)\cr total number of pages.
#' @param ... arguments passed on to [decorate_grob()].
#'
#' @return Closure that increments the page number.
#'
#' @keywords internal
decorate_grob_factory <- function(npages, ...) {
  current_page <- 0
  function(grob) {
    current_page <<- current_page + 1
    if (current_page > npages) {
      stop(paste("current page is", current_page, "but max.", npages, "specified."))
    }
    decorate_grob(grob = grob, page = paste("Page", current_page, "of", npages), ...)
  }
}

#' Decorate set of `grob`s and add page numbering
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Note that this uses the [decorate_grob_factory()] function.
#'
#' @param grobs (`list` of `grob`)\cr a list of grid grobs.
#' @param ... arguments passed on to [decorate_grob()].
#'
#' @return A decorated grob.
#'
#' @examples
#' library(ggplot2)
#' library(grid)
#' g <- with(data = iris, {
#'   list(
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Length, Sepal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Length, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Length, Petal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Length, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Sepal.Width, Petal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     ),
#'     ggplot2::ggplotGrob(
#'       ggplot2::ggplot(mapping = aes(Petal.Length, Petal.Width, col = Species)) +
#'         ggplot2::geom_point()
#'     )
#'   )
#' })
#' lg <- decorate_grob_set(grobs = g, titles = "Hello\nOne\nTwo\nThree", footnotes = "")
#'
#' draw_grob(lg[[1]])
#' draw_grob(lg[[2]])
#' draw_grob(lg[[6]])
#'
#' @export
decorate_grob_set <- function(grobs, ...) {
  n <- length(grobs)
  lgf <- decorate_grob_factory(npages = n, ...)
  lapply(grobs, lgf)
}

#' Horizontal waterfall plot
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This basic waterfall plot visualizes a quantity `height` ordered by value with some markup.
#'
#' @param height (`numeric`)\cr vector containing values to be plotted as the waterfall bars.
#' @param id (`character`)\cr vector containing identifiers to use as the x-axis label for the waterfall bars.
#' @param col (`character`)\cr color(s).
#' @param col_var (`factor`, `character`, or `NULL`)\cr categorical variable for bar coloring. `NULL` by default.
#' @param xlab (`string`)\cr x label. Default is `"ID"`.
#' @param ylab (`string`)\cr y label. Default is `"Value"`.
#' @param title (`string`)\cr text to be displayed as plot title.
#' @param col_legend_title (`string`)\cr text to be displayed as legend title.
#'
#' @return A `ggplot` waterfall plot.
#'
#' @examples
#' library(dplyr)
#'
#' g_waterfall(height = c(3, 5, -1), id = letters[1:3])
#'
#' g_waterfall(
#'   height = c(3, 5, -1),
#'   id = letters[1:3],
#'   col_var = letters[1:3]
#' )
#'
#' adsl_f <- tern_ex_adsl %>%
#'   select(USUBJID, STUDYID, ARM, ARMCD, SEX)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(PARAMCD == "OVRINV") %>%
#'   mutate(pchg = rnorm(n(), 10, 50))
#'
#' adrs_f <- head(adrs_f, 30)
#' adrs_f <- adrs_f[!duplicated(adrs_f$USUBJID), ]
#' head(adrs_f)
#'
#' g_waterfall(
#'   height = adrs_f$pchg,
#'   id = adrs_f$USUBJID,
#'   col_var = adrs_f$AVALC
#' )
#'
#' g_waterfall(
#'   height = adrs_f$pchg,
#'   id = paste("asdfdsfdsfsd", adrs_f$USUBJID),
#'   col_var = adrs_f$SEX
#' )
#'
#' g_waterfall(
#'   height = adrs_f$pchg,
#'   id = paste("asdfdsfdsfsd", adrs_f$USUBJID),
#'   xlab = "ID",
#'   ylab = "Percentage Change",
#'   title = "Waterfall plot"
#' )
#'
#' @export
g_waterfall <- function(height,
                        id,
                        col_var = NULL,
                        col = getOption("ggplot2.discrete.colour"),
                        xlab = NULL,
                        ylab = NULL,
                        col_legend_title = NULL,
                        title = NULL) {
  if (!is.null(col_var)) {
    check_same_n(height = height, id = id, col_var = col_var)
  } else {
    check_same_n(height = height, id = id)
  }

  checkmate::assert_multi_class(col_var, c("character", "factor"), null.ok = TRUE)
  checkmate::assert_character(col, null.ok = TRUE)

  xlabel <- deparse(substitute(id))
  ylabel <- deparse(substitute(height))

  col_label <- if (!missing(col_var)) {
    deparse(substitute(col_var))
  }

  xlab <- if (is.null(xlab)) xlabel else xlab
  ylab <- if (is.null(ylab)) ylabel else ylab
  col_legend_title <- if (is.null(col_legend_title)) col_label else col_legend_title

  plot_data <- data.frame(
    height = height,
    id = as.character(id),
    col_var = if (is.null(col_var)) "x" else to_n(col_var, length(height)),
    stringsAsFactors = FALSE
  )

  plot_data_ord <- plot_data[order(plot_data$height, decreasing = TRUE), ]

  p <- ggplot2::ggplot(plot_data_ord, ggplot2::aes(x = factor(id, levels = id), y = height)) +
    ggplot2::geom_col() +
    ggplot2::geom_text(
      label = format(plot_data_ord$height, digits = 2),
      vjust = ifelse(plot_data_ord$height >= 0, -0.5, 1.5)
    ) +
    ggplot2::xlab(xlab) +
    ggplot2::ylab(ylab) +
    ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 0, vjust = .5))

  if (!is.null(col_var)) {
    p <- p +
      ggplot2::aes(fill = col_var) +
      ggplot2::labs(fill = col_legend_title) +
      ggplot2::theme(
        legend.position = "bottom",
        legend.background = ggplot2::element_blank(),
        legend.title = ggplot2::element_text(face = "bold"),
        legend.box.background = ggplot2::element_rect(colour = "black")
      )
  }

  if (!is.null(col)) {
    p <- p +
      ggplot2::scale_fill_manual(values = col)
  }

  if (!is.null(title)) {
    p <- p +
      ggplot2::labs(title = title) +
      ggplot2::theme(plot.title = ggplot2::element_text(face = "bold"))
  }

  p
}

#' Cox regression helper function for interactions
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Test and estimate the effect of a treatment in interaction with a covariate.
#' The effect is estimated as the HR of the tested treatment for a given level
#' of the covariate, in comparison to the treatment control.
#'
#' @inheritParams argument_convention
#' @param x (`numeric` or `factor`)\cr the values of the covariate to be tested.
#' @param effect (`string`)\cr the name of the effect to be tested and estimated.
#' @param covar (`string`)\cr the name of the covariate in the model.
#' @param mod (`coxph`)\cr the Cox regression model.
#' @param label (`string`)\cr the label to be returned as `term_label`.
#' @param control (`list`)\cr a list of controls as returned by [control_coxreg()].
#' @param ... see methods.
#'
#' @examples
#' library(survival)
#'
#' set.seed(1, kind = "Mersenne-Twister")
#'
#' # Testing dataset [survival::bladder].
#' dta_bladder <- with(
#'   data = bladder[bladder$enum < 5, ],
#'   data.frame(
#'     time = stop,
#'     status = event,
#'     armcd = as.factor(rx),
#'     covar1 = as.factor(enum),
#'     covar2 = factor(
#'       sample(as.factor(enum)),
#'       levels = 1:4,
#'       labels = c("F", "F", "M", "M")
#'     )
#'   )
#' )
#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
#'
#' plot(
#'   survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
#'   lty = 2:4,
#'   xlab = "Months",
#'   col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
#' )
#'
#' @name cox_regression_inter
NULL

#' @describeIn cox_regression_inter S3 generic helper function to determine interaction effect.
#'
#' @return
#' * `h_coxreg_inter_effect()` returns a `data.frame` of covariate interaction effects consisting of the following
#'   variables: `effect`, `term`, `term_label`, `level`, `n`, `hr`, `lcl`, `ucl`, `pval`, and `pval_inter`.
#'
#' @export
h_coxreg_inter_effect <- function(x,
                                  effect,
                                  covar,
                                  mod,
                                  label,
                                  control,
                                  ...) {
  UseMethod("h_coxreg_inter_effect", x)
}

#' @describeIn cox_regression_inter Method for `numeric` class. Estimates the interaction with a `numeric` covariate.
#'
#' @method h_coxreg_inter_effect numeric
#'
#' @param at (`list`)\cr a list with items named after the covariate, every
#'   item is a vector of levels at which the interaction should be estimated.
#'
#' @export
h_coxreg_inter_effect.numeric <- function(x,
                                          effect,
                                          covar,
                                          mod,
                                          label,
                                          control,
                                          at,
                                          ...) {
  betas <- stats::coef(mod)
  attrs <- attr(stats::terms(mod), "term.labels")
  term_indices <- grep(
    pattern = effect,
    x = attrs[!grepl("strata\\(", attrs)]
  )
  checkmate::assert_vector(term_indices, len = 2)
  betas <- betas[term_indices]
  betas_var <- diag(stats::vcov(mod))[term_indices]
  betas_cov <- stats::vcov(mod)[term_indices[1], term_indices[2]]
  xval <- if (is.null(at[[covar]])) {
    stats::median(x)
  } else {
    at[[covar]]
  }
  effect_index <- !grepl(covar, names(betas))
  coef_hat <- betas[effect_index] + xval * betas[!effect_index]
  coef_se <- sqrt(
    betas_var[effect_index] +
      xval ^ 2 * betas_var[!effect_index] + # styler: off
      2 * xval * betas_cov
  )
  q_norm <- stats::qnorm((1 + control$conf_level) / 2)
  data.frame(
    effect = "Covariate:",
    term = rep(covar, length(xval)),
    term_label = paste0("  ", xval),
    level = as.character(xval),
    n = NA,
    hr = exp(coef_hat),
    lcl = exp(coef_hat - q_norm * coef_se),
    ucl = exp(coef_hat + q_norm * coef_se),
    pval = NA,
    pval_inter = NA,
    stringsAsFactors = FALSE
  )
}

#' @describeIn cox_regression_inter Method for `factor` class. Estimate the interaction with a `factor` covariate.
#'
#' @method h_coxreg_inter_effect factor
#'
#' @param data (`data.frame`)\cr the data frame on which the model was fit.
#'
#' @export
h_coxreg_inter_effect.factor <- function(x,
                                         effect,
                                         covar,
                                         mod,
                                         label,
                                         control,
                                         data,
                                         ...) {
  lvl_given <- levels(x)
  y <- h_coxreg_inter_estimations(
    variable = effect, given = covar,
    lvl_var = levels(data[[effect]]),
    lvl_given = lvl_given,
    mod = mod,
    conf_level = 0.95
  )[[1]]

  data.frame(
    effect = "Covariate:",
    term = rep(covar, nrow(y)),
    term_label = paste0("  ", lvl_given),
    level = lvl_given,
    n = NA,
    hr = y[, "hr"],
    lcl = y[, "lcl"],
    ucl = y[, "ucl"],
    pval = NA,
    pval_inter = NA,
    stringsAsFactors = FALSE
  )
}

#' @describeIn cox_regression_inter Method for `character` class. Estimate the interaction with a `character` covariate.
#'   This makes an automatic conversion to `factor` and then forwards to the method for factors.
#'
#' @method h_coxreg_inter_effect character
#'
#' @note
#' * Automatic conversion of character to factor does not guarantee results can be generated correctly. It is
#'   therefore better to always pre-process the dataset such that factors are manually created from character
#'   variables before passing the dataset to [rtables::build_table()].
#'
#' @export
h_coxreg_inter_effect.character <- function(x,
                                            effect,
                                            covar,
                                            mod,
                                            label,
                                            control,
                                            data,
                                            ...) {
  y <- as.factor(x)

  h_coxreg_inter_effect(
    x = y,
    effect = effect,
    covar = covar,
    mod = mod,
    label = label,
    control = control,
    data = data,
    ...
  )
}

#' @describeIn cox_regression_inter A higher level function to get
#'   the results of the interaction test and the estimated values.
#'
#' @return
#' * `h_coxreg_extract_interaction()` returns the result of an interaction test and the estimated values. If
#'   no interaction, [h_coxreg_univar_extract()] is applied instead.
#'
#' @examples
#' mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder)
#' h_coxreg_extract_interaction(
#'   mod = mod, effect = "armcd", covar = "covar1", data = dta_bladder,
#'   control = control_coxreg()
#' )
#'
#' @export
h_coxreg_extract_interaction <- function(effect,
                                         covar,
                                         mod,
                                         data,
                                         at,
                                         control) {
  if (!any(attr(stats::terms(mod), "order") == 2)) {
    y <- h_coxreg_univar_extract(
      effect = effect, covar = covar, mod = mod, data = data, control = control
    )
    y$pval_inter <- NA
    y
  } else {
    test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]

    # Test the main treatment effect.
    mod_aov <- muffled_car_anova(mod, test_statistic)
    sum_anova <- broom::tidy(mod_aov)
    pval <- sum_anova[sum_anova$term == effect, ][["p.value"]]

    # Test the interaction effect.
    pval_inter <- sum_anova[grep(":", sum_anova$term), ][["p.value"]]
    covar_test <- data.frame(
      effect = "Covariate:",
      term = covar,
      term_label = unname(labels_or_names(data[covar])),
      level = "",
      n = mod$n, hr = NA, lcl = NA, ucl = NA, pval = pval,
      pval_inter = pval_inter,
      stringsAsFactors = FALSE
    )
    # Estimate the interaction.
    y <- h_coxreg_inter_effect(
      data[[covar]],
      covar = covar,
      effect = effect,
      mod = mod,
      label = unname(labels_or_names(data[covar])),
      at = at,
      control = control,
      data = data
    )
    rbind(covar_test, y)
  }
}

#' @describeIn cox_regression_inter Hazard ratio estimation in interactions.
#'
#' @param variable,given (`string`)\cr the name of variables in interaction. We seek the estimation
#'   of the levels of `variable` given the levels of `given`.
#' @param lvl_var,lvl_given (`character`)\cr corresponding levels as given by [levels()].
#' @param mod (`coxph`)\cr a fitted Cox regression model (see [survival::coxph()]).
#'
#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)
#'   and Sex (F, M; reference Female) and the model being abbreviated: y ~ Arm + Sex + Arm:Sex.
#'   The cox regression estimates the coefficients along with a variance-covariance matrix for:
#'
#'   - b1 (arm b), b2 (arm c)
#'   - b3 (sex m)
#'   - b4 (arm b: sex m), b5 (arm c: sex m)
#'
#'   The estimation of the Hazard Ratio for arm C/sex M is given in reference
#'   to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5).
#'   The interaction coefficient is deduced by b2 + b5 while the standard error
#'   is obtained as $sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$.
#'
#' @return
#' * `h_coxreg_inter_estimations()` returns a list of matrices (one per level of variable) with rows corresponding
#'   to the combinations of `variable` and `given`, with columns:
#'   * `coef_hat`: Estimation of the coefficient.
#'   * `coef_se`: Standard error of the estimation.
#'   * `hr`: Hazard ratio.
#'   * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.
#'
#' @examples
#' mod <- coxph(Surv(time, status) ~ armcd * covar1, data = dta_bladder)
#' result <- h_coxreg_inter_estimations(
#'   variable = "armcd", given = "covar1",
#'   lvl_var = levels(dta_bladder$armcd),
#'   lvl_given = levels(dta_bladder$covar1),
#'   mod = mod, conf_level = .95
#' )
#' result
#'
#' @export
h_coxreg_inter_estimations <- function(variable,
                                       given,
                                       lvl_var,
                                       lvl_given,
                                       mod,
                                       conf_level = 0.95) {
  var_lvl <- paste0(variable, lvl_var[-1]) # [-1]: reference level
  giv_lvl <- paste0(given, lvl_given)
  design_mat <- expand.grid(variable = var_lvl, given = giv_lvl)
  design_mat <- design_mat[order(design_mat$variable, design_mat$given), ]
  design_mat <- within(
    data = design_mat,
    expr = {
      inter <- paste0(variable, ":", given)
      rev_inter <- paste0(given, ":", variable)
    }
  )
  split_by_variable <- design_mat$variable
  interaction_names <- paste(design_mat$variable, design_mat$given, sep = "/")

  mmat <- stats::model.matrix(mod)[1, ]
  mmat[!mmat == 0] <- 0

  design_mat <- apply(
    X = design_mat, MARGIN = 1, FUN = function(x) {
      mmat[names(mmat) %in% x[-which(names(x) == "given")]] <- 1
      mmat
    }
  )
  colnames(design_mat) <- interaction_names

  coef <- stats::coef(mod)
  vcov <- stats::vcov(mod)
  betas <- as.matrix(coef)
  coef_hat <- t(design_mat) %*% betas
  dimnames(coef_hat)[2] <- "coef"
  coef_se <- apply(
    design_mat, 2,
    function(x) {
      vcov_el <- as.logical(x)
      y <- vcov[vcov_el, vcov_el]
      y <- sum(y)
      y <- sqrt(y)
      return(y)
    }
  )
  q_norm <- stats::qnorm((1 + conf_level) / 2)
  y <- cbind(coef_hat, `se(coef)` = coef_se)
  y <- apply(y, 1, function(x) {
    x["hr"] <- exp(x["coef"])
    x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
    x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])
    x
  })
  y <- t(y)
  y <- by(y, split_by_variable, identity)
  y <- lapply(y, as.matrix)
  attr(y, "details") <- paste0(
    "Estimations of ", variable,
    " hazard ratio given the level of ", given, " compared to ",
    variable, " level ", lvl_var[1], "."
  )
  y
}

#' Survival time point analysis
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [surv_timepoint()] creates a layout element to analyze patient survival rates and difference
#' of survival rates between groups at a given time point. The primary analysis variable `vars` is the time variable.
#' Other required inputs are `time_point`, the numeric time point of interest, and `is_event`, a variable that
#' indicates whether or not an event has occurred. The `method` argument is used to specify whether you want to analyze
#' survival estimations (`"surv"`), difference in survival with the control (`"surv_diff"`), or both of these
#' (`"both"`).
#'
#' @inheritParams argument_convention
#' @inheritParams s_surv_time
#' @param time_point (`numeric(1)`)\cr survival time point of interest.
#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
#'   [control_surv_timepoint()]. Some possible parameter options are:
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for survival rate.
#'   * `conf_type` (`string`)\cr confidence interval type. Options are "plain" (default), "log", "log-log",
#'     see more in [survival::survfit()]. Note option "none" is no longer supported.
#' @param method (`string`)\cr `"surv"` (survival estimations), `"surv_diff"` (difference in survival with the
#'   control), or `"both"`.
#' @param table_names_suffix (`string`)\cr optional suffix for the `table_names` used for the `rtables` to
#'   avoid warnings from duplicate table names.
#' @param .indent_mods (named `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("surv_timepoint"), type = "sh")``
#'
#' @name survival_timepoint
#' @order 1
NULL

#' @describeIn survival_timepoint Statistics function which analyzes survival rate.
#'
#' @return
#' * `s_surv_timepoint()` returns the statistics:
#'   * `pt_at_risk`: Patients remaining at risk.
#'   * `event_free_rate`: Event-free rate (%).
#'   * `rate_se`: Standard error of event free rate.
#'   * `rate_ci`: Confidence interval for event free rate.
#'   * `event_free_rate_3d`: Event-free rate (%) with Confidence interval.
#'
#' @keywords internal
s_surv_timepoint <- function(df,
                             .var,
                             time_point,
                             is_event,
                             control = control_surv_timepoint()) {
  checkmate::assert_string(.var)
  assert_df_with_variables(df, list(tte = .var, is_event = is_event))
  checkmate::assert_numeric(df[[.var]], min.len = 1, any.missing = FALSE)
  checkmate::assert_number(time_point)
  checkmate::assert_logical(df[[is_event]], min.len = 1, any.missing = FALSE)

  conf_type <- control$conf_type
  conf_level <- control$conf_level

  formula <- stats::as.formula(paste0("survival::Surv(", .var, ", ", is_event, ") ~ 1"))
  srv_fit <- survival::survfit(
    formula = formula,
    data = df,
    conf.int = conf_level,
    conf.type = conf_type
  )
  s_srv_fit <- summary(srv_fit, times = time_point, extend = TRUE)
  df_srv_fit <- as.data.frame(s_srv_fit[c("time", "n.risk", "surv", "lower", "upper", "std.err")])
  if (df_srv_fit[["n.risk"]] == 0) {
    pt_at_risk <- event_free_rate <- rate_se <- NA_real_
    rate_ci <- c(NA_real_, NA_real_)
  } else {
    pt_at_risk <- df_srv_fit$n.risk
    event_free_rate <- df_srv_fit$surv
    rate_se <- df_srv_fit$std.err
    rate_ci <- c(df_srv_fit$lower, df_srv_fit$upper)
  }
  event_free_rate_3d <- c(event_free_rate, rate_ci)
  list(
    pt_at_risk = formatters::with_label(pt_at_risk, "Patients remaining at risk"),
    event_free_rate = formatters::with_label(event_free_rate * 100, "Event Free Rate (%)"),
    rate_se = formatters::with_label(rate_se * 100, "Standard Error of Event Free Rate"),
    rate_ci = formatters::with_label(rate_ci * 100, f_conf_level(conf_level)),
    event_free_rate_3d = formatters::with_label(
      event_free_rate_3d * 100, paste0("Event Free Rate (", f_conf_level(conf_level), ")")
    )
  )
}

#' @describeIn survival_timepoint Formatted analysis function which is used as `afun` in `surv_timepoint()`
#'   when `method = "surv"`.
#'
#' @return
#' * `a_surv_timepoint()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_surv_timepoint <- make_afun(
  s_surv_timepoint,
  .indent_mods = c(
    pt_at_risk = 0L,
    event_free_rate = 0L,
    rate_se = 1L,
    rate_ci = 1L
  ),
  .formats = c(
    pt_at_risk = "xx",
    event_free_rate = "xx.xx",
    rate_se = "xx.xx",
    rate_ci = "(xx.xx, xx.xx)"
  )
)

#' @describeIn survival_timepoint Statistics function which analyzes difference between two survival rates.
#'
#' @return
#' * `s_surv_timepoint_diff()` returns the statistics:
#'   * `rate_diff`: Event-free rate difference between two groups.
#'   * `rate_diff_ci`: Confidence interval for the difference.
#'   * `rate_diff_ci_3d`: Event-free rate difference and confidence interval between two groups.
#'   * `ztest_pval`: p-value to test the difference is 0.
#'
#' @keywords internal
s_surv_timepoint_diff <- function(df,
                                  .var,
                                  .ref_group,
                                  .in_ref_col,
                                  time_point,
                                  control = control_surv_timepoint(),
                                  ...) {
  if (.in_ref_col) {
    return(
      list(
        rate_diff = formatters::with_label("", "Difference in Event Free Rate"),
        rate_diff_ci = formatters::with_label("", f_conf_level(control$conf_level)),
        rate_diff_ci_3d = formatters::with_label(
          "", paste0("Difference in Event Free Rate", f_conf_level(control$conf_level))
        ),
        ztest_pval = formatters::with_label("", "p-value (Z-test)")
      )
    )
  }
  data <- rbind(.ref_group, df)
  group <- factor(rep(c("ref", "x"), c(nrow(.ref_group), nrow(df))), levels = c("ref", "x"))
  res_per_group <- lapply(split(data, group), function(x) {
    s_surv_timepoint(df = x, .var = .var, time_point = time_point, control = control, ...)
  })

  res_x <- res_per_group[[2]]
  res_ref <- res_per_group[[1]]
  rate_diff <- res_x$event_free_rate - res_ref$event_free_rate
  se_diff <- sqrt(res_x$rate_se^2 + res_ref$rate_se^2)

  qs <- c(-1, 1) * stats::qnorm(1 - (1 - control$conf_level) / 2)
  rate_diff_ci <- rate_diff + qs * se_diff
  rate_diff_ci_3d <- c(rate_diff, rate_diff_ci)
  ztest_pval <- if (is.na(rate_diff)) {
    NA
  } else {
    2 * (1 - stats::pnorm(abs(rate_diff) / se_diff))
  }
  list(
    rate_diff = formatters::with_label(rate_diff, "Difference in Event Free Rate"),
    rate_diff_ci = formatters::with_label(rate_diff_ci, f_conf_level(control$conf_level)),
    rate_diff_ci_3d = formatters::with_label(
      rate_diff_ci_3d, paste0("Difference in Event Free Rate", f_conf_level(control$conf_level))
    ),
    ztest_pval = formatters::with_label(ztest_pval, "p-value (Z-test)")
  )
}

#' @describeIn survival_timepoint Formatted analysis function which is used as `afun` in `surv_timepoint()`
#'   when `method = "surv_diff"`.
#'
#' @return
#' * `a_surv_timepoint_diff()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_surv_timepoint_diff <- make_afun(
  s_surv_timepoint_diff,
  .formats = c(
    rate_diff = "xx.xx",
    rate_diff_ci = "(xx.xx, xx.xx)",
    rate_diff_ci_3d = format_xx("xx.xx (xx.xx, xx.xx)"),
    ztest_pval = "x.xxxx | (<0.0001)"
  )
)

#' @describeIn survival_timepoint Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `surv_timepoint()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_surv_timepoint()` and/or `s_surv_timepoint_diff()` to the table layout depending on
#'   the value of `method`.
#'
#' @examples
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(
#'     AVAL = day2month(AVAL),
#'     is_event = CNSR == 0
#'   )
#'
#' # Survival at given time points.
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   surv_timepoint(
#'     vars = "AVAL",
#'     var_labels = "Months",
#'     is_event = "is_event",
#'     time_point = 7
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' # Difference in survival at given time points.
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   surv_timepoint(
#'     vars = "AVAL",
#'     var_labels = "Months",
#'     is_event = "is_event",
#'     time_point = 9,
#'     method = "surv_diff",
#'     .indent_mods = c("rate_diff" = 0L, "rate_diff_ci" = 2L, "ztest_pval" = 2L)
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' # Survival and difference in survival at given time points.
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   surv_timepoint(
#'     vars = "AVAL",
#'     var_labels = "Months",
#'     is_event = "is_event",
#'     time_point = 9,
#'     method = "both"
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
#' @order 2
surv_timepoint <- function(lyt,
                           vars,
                           time_point,
                           is_event,
                           control = control_surv_timepoint(),
                           method = c("surv", "surv_diff", "both"),
                           na_str = default_na_str(),
                           nested = TRUE,
                           ...,
                           table_names_suffix = "",
                           var_labels = "Time",
                           show_labels = "visible",
                           .stats = c(
                             "pt_at_risk", "event_free_rate", "rate_ci",
                             "rate_diff", "rate_diff_ci", "ztest_pval"
                           ),
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = if (method == "both") {
                             c(rate_diff = 1L, rate_diff_ci = 2L, ztest_pval = 2L)
                           } else {
                             c(rate_diff_ci = 1L, ztest_pval = 1L)
                           }) {
  method <- match.arg(method)
  checkmate::assert_string(table_names_suffix)

  extra_args <- list(time_point = time_point, is_event = is_event, control = control, ...)

  f <- list(
    surv = c("pt_at_risk", "event_free_rate", "rate_se", "rate_ci", "event_free_rate_3d"),
    surv_diff = c("rate_diff", "rate_diff_ci", "ztest_pval", "rate_diff_ci_3d")
  )
  .stats <- h_split_param(.stats, .stats, f = f)
  .formats <- h_split_param(.formats, names(.formats), f = f)
  .labels <- h_split_param(.labels, names(.labels), f = f)
  .indent_mods <- h_split_param(.indent_mods, names(.indent_mods), f = f)

  afun_surv <- make_afun(
    a_surv_timepoint,
    .stats = .stats$surv,
    .formats = .formats$surv,
    .labels = .labels$surv,
    .indent_mods = .indent_mods$surv
  )

  afun_surv_diff <- make_afun(
    a_surv_timepoint_diff,
    .stats = .stats$surv_diff,
    .formats = .formats$surv_diff,
    .labels = .labels$surv_diff,
    .indent_mods = .indent_mods$surv_diff
  )

  time_point <- extra_args$time_point

  for (i in seq_along(time_point)) {
    extra_args[["time_point"]] <- time_point[i]

    if (method %in% c("surv", "both")) {
      lyt <- analyze(
        lyt,
        vars,
        var_labels = paste(time_point[i], var_labels),
        table_names = paste0("surv_", time_point[i], table_names_suffix),
        show_labels = show_labels,
        afun = afun_surv,
        na_str = na_str,
        nested = nested,
        extra_args = extra_args
      )
    }

    if (method %in% c("surv_diff", "both")) {
      lyt <- analyze(
        lyt,
        vars,
        var_labels = paste(time_point[i], var_labels),
        table_names = paste0("surv_diff_", time_point[i], table_names_suffix),
        show_labels = ifelse(method == "both", "hidden", show_labels),
        afun = afun_surv_diff,
        na_str = na_str,
        nested = nested,
        extra_args = extra_args
      )
    }
  }
  lyt
}

#' Count number of patients with missed doses by thresholds
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function creates a layout element to calculate cumulative counts of patients with number of missed
#' doses at least equal to user-specified threshold values.
#'
#' This function analyzes numeric variable `vars`, a variable with numbers of missed doses,
#' against the threshold values supplied to the `thresholds` argument as a numeric vector. This function
#' assumes that every row of the given data frame corresponds to a unique patient.
#'
#' @inheritParams s_count_cumulative
#' @inheritParams argument_convention
#' @param thresholds (`numeric`)\cr minimum number of missed doses the patients had.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_missed_doses"), type = "sh")``
#'
#' @seealso
#' * Relevant description function [d_count_missed_doses()] which generates labels for [count_missed_doses()].
#' * Similar analyze function [count_cumulative()] which more generally counts cumulative values and has more
#'   options for threshold handling, but uses different labels.
#'
#' @name count_missed_doses
#' @order 1
NULL

#' @describeIn count_missed_doses Statistics function to count non-missing values.
#'
#' @return
#' * `s_count_nonmissing()` returns the statistic `n` which is the count of non-missing values in `x`.
#'
#' @keywords internal
s_count_nonmissing <- function(x) {
  list(n = n_available(x))
}

#' Description function that calculates labels for `s_count_missed_doses()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams s_count_missed_doses
#'
#' @return [d_count_missed_doses()] returns a named `character` vector with the labels.
#'
#' @seealso [s_count_missed_doses()]
#'
#' @export
d_count_missed_doses <- function(thresholds) {
  paste0("At least ", thresholds, " missed dose", ifelse(thresholds > 1, "s", ""))
}

#' @describeIn count_missed_doses Statistics function to count patients with missed doses.
#'
#' @return
#' * `s_count_missed_doses()` returns the statistics `n` and `count_fraction` with one element for each threshold.
#'
#' @keywords internal
s_count_missed_doses <- function(x,
                                 thresholds,
                                 .N_col, # nolint
                                 .N_row, # nolint
                                 denom = c("N_col", "n", "N_row")) {
  stat <- s_count_cumulative(
    x = x,
    thresholds = thresholds,
    lower_tail = FALSE,
    include_eq = TRUE,
    .N_col = .N_col,
    .N_row = .N_row,
    denom = denom
  )
  labels <- d_count_missed_doses(thresholds)
  for (i in seq_along(stat$count_fraction)) {
    stat$count_fraction[[i]] <- formatters::with_label(stat$count_fraction[[i]], label = labels[i])
  }
  n_stat <- s_count_nonmissing(x)
  c(n_stat, stat)
}

#' @describeIn count_missed_doses Formatted analysis function which is used as `afun`
#'   in `count_missed_doses()`.
#'
#' @return
#' * `a_count_missed_doses()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_missed_doses <- make_afun(
  s_count_missed_doses,
  .formats = c(n = "xx", count_fraction = format_count_fraction)
)

#' @describeIn count_missed_doses Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_missed_doses()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_missed_doses()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' anl <- tern_ex_adsl %>%
#'   distinct(STUDYID, USUBJID, ARM) %>%
#'   mutate(
#'     PARAMCD = "TNDOSMIS",
#'     PARAM = "Total number of missed doses during study",
#'     AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),
#'     AVALC = ""
#'   )
#'
#' basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_missed_doses("AVAL", thresholds = c(1, 5, 10, 15), var_labels = "Missed Doses") %>%
#'   build_table(anl, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_missed_doses <- function(lyt,
                               vars,
                               thresholds,
                               var_labels = vars,
                               show_labels = "visible",
                               na_str = default_na_str(),
                               nested = TRUE,
                               ...,
                               table_names = vars,
                               .stats = NULL,
                               .formats = NULL,
                               .labels = NULL,
                               .indent_mods = NULL) {
  extra_args <- list(thresholds = thresholds, ...)

  afun <- make_afun(
    a_count_missed_doses,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "count_fraction"
  )
  analyze(
    lyt = lyt,
    vars = vars,
    afun = afun,
    var_labels = var_labels,
    table_names = table_names,
    show_labels = show_labels,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Control function for Cox-PH model
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for Cox-PH model, typically used internally to specify
#' details of Cox-PH model for [s_coxph_pairwise()]. `conf_level` refers to Hazard Ratio estimation.
#'
#' @inheritParams argument_convention
#' @param pval_method (`string`)\cr p-value method for testing hazard ratio = 1.
#'   Default method is `"log-rank"`, can also be set to `"wald"` or `"likelihood"`.
#' @param ties (`string`)\cr string specifying the method for tie handling. Default is `"efron"`,
#'   can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()].
#'
#' @return A list of components with the same names as the arguments.
#'
#' @export
control_coxph <- function(pval_method = c("log-rank", "wald", "likelihood"),
                          ties = c("efron", "breslow", "exact"),
                          conf_level = 0.95) {
  pval_method <- match.arg(pval_method)
  ties <- match.arg(ties)
  assert_proportion_value(conf_level)

  list(pval_method = pval_method, ties = ties, conf_level = conf_level)
}

#' Control function for `survfit` models for survival time
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for `survfit` model, typically used internally to specify
#' details of `survfit` model for [s_surv_time()]. `conf_level` refers to survival time estimation.
#'
#' @inheritParams argument_convention
#' @param conf_type (`string`)\cr confidence interval type. Options are "plain" (default), "log", "log-log",
#'   see more in [survival::survfit()]. Note option "none" is no longer supported.
#' @param quantiles (`numeric(2)`)\cr vector of length two specifying the quantiles of survival time.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @export
control_surv_time <- function(conf_level = 0.95,
                              conf_type = c("plain", "log", "log-log"),
                              quantiles = c(0.25, 0.75)) {
  conf_type <- match.arg(conf_type)
  checkmate::assert_numeric(quantiles, lower = 0, upper = 1, len = 2, unique = TRUE, sorted = TRUE)
  nullo <- lapply(quantiles, assert_proportion_value)
  assert_proportion_value(conf_level)
  list(conf_level = conf_level, conf_type = conf_type, quantiles = quantiles)
}

#' Control function for `survfit` models for patients' survival rate at time points
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for `survfit` model, typically used internally to specify
#' details of `survfit` model for [s_surv_timepoint()]. `conf_level` refers to patient risk estimation at a time point.
#'
#' @inheritParams argument_convention
#' @inheritParams control_surv_time
#'
#' @return A list of components with the same names as the arguments.
#'
#' @export
control_surv_timepoint <- function(conf_level = 0.95,
                                   conf_type = c("plain", "log", "log-log")) {
  conf_type <- match.arg(conf_type)
  assert_proportion_value(conf_level)
  list(
    conf_level = conf_level,
    conf_type = conf_type
  )
}

#' Compare variables between groups
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [compare_vars()] creates a layout element to summarize and compare one or more variables, using
#' the S3 generic function [s_summary()] to calculate a list of summary statistics. A list of all available statistics
#' for numeric variables can be viewed by running `get_stats("analyze_vars_numeric", add_pval = TRUE)` and for
#' non-numeric variables by running `get_stats("analyze_vars_counts", add_pval = TRUE)`. Use the `.stats` parameter to
#' specify the statistics to include in your output summary table.
#'
#' Prior to using this function in your table layout you must use [rtables::split_cols_by()] to create a column
#' split on the variable to be used in comparisons, and specify a reference group via the `ref_group` parameter.
#' Comparisons can be performed for each group (column) against the specified reference group by including the p-value
#' statistic.
#'
#' @inheritParams argument_convention
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'  Options for numeric variables are: ``r shQuote(get_stats("analyze_vars_numeric", add_pval = TRUE), type = "sh")``
#'
#'  Options for non-numeric variables are: ``r shQuote(get_stats("analyze_vars_counts", add_pval = TRUE), type = "sh")``
#'
#' @note
#' * For factor variables, `denom` for factor proportions can only be `n` since the purpose is to compare proportions
#'   between columns, therefore a row-based proportion would not make sense. Proportion based on `N_col` would
#'   be difficult since we use counts for the chi-squared test statistic, therefore missing values should be accounted
#'   for as explicit factor levels.
#' * If factor variables contain `NA`, these `NA` values are excluded by default. To include `NA` values
#'   set `na.rm = FALSE` and missing values will be displayed as an `NA` level. Alternatively, an explicit
#'   factor level can be defined for `NA` values during pre-processing via [df_explicit_na()] - the
#'   default `na_level` (`"<Missing>"`) will also be excluded when `na.rm` is set to `TRUE`.
#' * For character variables, automatic conversion to factor does not guarantee that the table
#'   will be generated correctly. In particular for sparse tables this very likely can fail.
#'   Therefore it is always better to manually convert character variables to factors during pre-processing.
#' * For `compare_vars()`, the column split must define a reference group via `ref_group` so that the comparison
#'   is well defined.
#'
#' @seealso [s_summary()] which is used internally to compute a summary within `s_compare()`, and [a_summary()]
#'   which is used (with `compare = TRUE`) as the analysis function for `compare_vars()`.
#'
#' @name compare_variables
#' @include analyze_variables.R
#' @order 1
NULL

#' @describeIn compare_variables S3 generic function to produce a comparison summary.
#'
#' @return
#' * `s_compare()` returns output of [s_summary()] and comparisons versus the reference group in the form of p-values.
#'
#' @export
s_compare <- function(x,
                      ...) {
  UseMethod("s_compare", x)
}

#' @describeIn compare_variables Method for `numeric` class. This uses the standard t-test
#'   to calculate the p-value.
#'
#' @method s_compare numeric
#'
#' @examples
#' # `s_compare.numeric`
#'
#' ## Usual case where both this and the reference group vector have more than 1 value.
#' s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE)
#'
#' ## If one group has not more than 1 value, then p-value is not calculated.
#' s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE)
#'
#' ## Empty numeric does not fail, it returns NA-filled items and no p-value.
#' s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE)
#'
#' @export
s_compare.numeric <- function(x, ...) {
  s_summary.numeric(x = x, compare_with_ref_group = TRUE, ...)
}

#' @describeIn compare_variables Method for `factor` class. This uses the chi-squared test
#'   to calculate the p-value.
#'
#' @method s_compare factor
#'
#' @examples
#' # `s_compare.factor`
#'
#' ## Basic usage:
#' x <- factor(c("a", "a", "b", "c", "a"))
#' y <- factor(c("a", "b", "c"))
#' s_compare(x = x, .ref_group = y, .in_ref_col = FALSE)
#'
#' ## Management of NA values.
#' x <- explicit_na(factor(c("a", "a", "b", "c", "a", NA, NA)))
#' y <- explicit_na(factor(c("a", "b", "c", NA)))
#' s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na_rm = TRUE)
#' s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na_rm = FALSE)
#'
#' @export
s_compare.factor <- function(x, ...) {
  s_summary.factor(
    x = x,
    compare_with_ref_group = TRUE,
    ...
  )
}

#' @describeIn compare_variables Method for `character` class. This makes an automatic
#'   conversion to `factor` (with a warning) and then forwards to the method for factors.
#'
#' @method s_compare character
#'
#' @examples
#' # `s_compare.character`
#'
#' ## Basic usage:
#' x <- c("a", "a", "b", "c", "a")
#' y <- c("a", "b", "c")
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = "x", verbose = FALSE)
#'
#' ## Note that missing values handling can make a large difference:
#' x <- c("a", "a", "b", "c", "a", NA)
#' y <- c("a", "b", "c", rep(NA, 20))
#' s_compare(x,
#'   .ref_group = y, .in_ref_col = FALSE,
#'   .var = "x", verbose = FALSE
#' )
#' s_compare(x,
#'   .ref_group = y, .in_ref_col = FALSE, .var = "x",
#'   na.rm = FALSE, verbose = FALSE
#' )
#'
#' @export
s_compare.character <- function(x, ...) {
  s_summary.character(
    x,
    compare_with_ref_group = TRUE,
    ...
  )
}

#' @describeIn compare_variables Method for `logical` class. A chi-squared test
#'   is used. If missing values are not removed, then they are counted as `FALSE`.
#'
#' @method s_compare logical
#'
#' @examples
#' # `s_compare.logical`
#'
#' ## Basic usage:
#' x <- c(TRUE, FALSE, TRUE, TRUE)
#' y <- c(FALSE, FALSE, TRUE)
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE)
#'
#' ## Management of NA values.
#' x <- c(NA, TRUE, FALSE)
#' y <- c(NA, NA, NA, NA, FALSE)
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE, na_rm = TRUE)
#' s_compare(x, .ref_group = y, .in_ref_col = FALSE, na_rm = FALSE)
#'
#' @export
s_compare.logical <- function(x, ...) {
  s_summary.logical(
    x = x,
    compare_with_ref_group = TRUE,
    ...
  )
}

#' @describeIn compare_variables Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @param ... additional arguments passed to `s_compare()`, including:
#'   * `denom`: (`string`) choice of denominator. Options are `c("n", "N_col", "N_row")`. For factor variables, can
#'     only be `"n"` (number of values in this row and column intersection).
#'   * `.N_row`: (`numeric(1)`) Row-wise N (row group count) for the group of observations being analyzed (i.e. with no
#'     column-based subsetting).
#'   * `.N_col`: (`numeric(1)`) Column-wise N (column count) for the full column being tabulated within.
#'   * `verbose`: (`flag`) Whether additional warnings and messages should be printed. Mainly used to print out
#'     information about factor casting. Defaults to `TRUE`. Used for `character`/`factor` variables only.
#' @param .indent_mods (named `integer`)\cr indent modifiers for the labels. Each element of the vector
#'   should be a name-value pair with name corresponding to a statistic specified in `.stats` and value the indentation
#'   for that statistic's row label.
#'
#' @return
#' * `compare_vars()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_compare()` to the table layout.
#'
#' @examples
#' # `compare_vars()` in `rtables` pipelines
#'
#' ## Default output within a `rtables` pipeline.
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", ref_group = "ARM B") %>%
#'   compare_vars(c("AGE", "SEX"))
#' build_table(lyt, tern_ex_adsl)
#'
#' ## Select and format statistics output.
#' lyt <- basic_table() %>%
#'   split_cols_by("ARMCD", ref_group = "ARM C") %>%
#'   compare_vars(
#'     vars = "AGE",
#'     .stats = c("mean_sd", "pval"),
#'     .formats = c(mean_sd = "xx.x, xx.x"),
#'     .labels = c(mean_sd = "Mean, SD")
#'   )
#' build_table(lyt, df = tern_ex_adsl)
#'
#' @export
#' @order 2
compare_vars <- function(lyt,
                         vars,
                         var_labels = vars,
                         na_str = default_na_str(),
                         nested = TRUE,
                         ...,
                         na_rm = TRUE,
                         show_labels = "default",
                         table_names = vars,
                         section_div = NA_character_,
                         .stats = c("n", "mean_sd", "count_fraction", "pval"),
                         .stat_names = NULL,
                         .formats = NULL,
                         .labels = NULL,
                         .indent_mods = NULL) {
  analyze_vars(
    lyt = lyt,
    compare_with_ref_group = TRUE,
    vars = vars,
    var_labels = var_labels,
    na_str = na_str,
    nested = nested,
    na_rm = na_rm,
    show_labels = show_labels,
    table_names = table_names,
    section_div = section_div,
    .stats = .stats,
    .stat_names = .stat_names,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    ...
  )
}

#' Count patient events in columns
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The summarize function [summarize_patients_events_in_cols()] creates a layout element to summarize patient
#' event counts in columns.
#'
#' This function analyzes the elements (events) supplied via the `filters_list` parameter and returns a row
#' with counts of number of patients for each event as well as the total numbers of patients and events.
#' The `id` variable is used to indicate unique subject identifiers (defaults to `USUBJID`).
#'
#' If there are multiple occurrences of the same event recorded for a patient, the event is only counted once.
#'
#' @inheritParams argument_convention
#' @param filters_list (named `list` of `character`)\cr list where each element in this list describes one
#'   type of event describe by filters, in the same format as [s_count_patients_with_event()].
#'   If it has a label, then this will be used for the column title.
#' @param empty_stats (`character`)\cr optional names of the statistics that should be returned empty such
#'   that corresponding table cells will stay blank.
#' @param custom_label (`string` or `NULL`)\cr if provided and `labelstr` is empty then this will
#'   be used as label.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   In addition to any statistics added using `filters_list`, statistic options are:
#'   ``r shQuote(get_stats("summarize_patients_events_in_cols"), type = "sh")``
#'
#' @name count_patients_events_in_cols
#' @order 1
NULL

#' @describeIn count_patients_events_in_cols Statistics function which counts numbers of patients and multiple
#'   events defined by filters. Used as analysis function `afun` in `summarize_patients_events_in_cols()`.
#'
#' @return
#' * `s_count_patients_and_multiple_events()` returns a list with the statistics:
#'   - `unique`: number of unique patients in `df`.
#'   - `all`: number of rows in `df`.
#'   - one element with the same name as in `filters_list`: number of rows in `df`,
#'     i.e. events, fulfilling the filter condition.
#'
#' @keywords internal
s_count_patients_and_multiple_events <- function(df, # nolint
                                                 id,
                                                 filters_list,
                                                 empty_stats = character(),
                                                 labelstr = "",
                                                 custom_label = NULL) {
  checkmate::assert_list(filters_list, names = "named")
  checkmate::assert_data_frame(df)
  checkmate::assert_string(id)
  checkmate::assert_disjunct(c("unique", "all"), names(filters_list))
  checkmate::assert_character(empty_stats)
  checkmate::assert_string(labelstr)
  checkmate::assert_string(custom_label, null.ok = TRUE)

  # Below we want to count each row in `df` once, therefore introducing this helper index column.
  df$.row_index <- as.character(seq_len(nrow(df)))
  y <- list()
  row_label <- if (labelstr != "") {
    labelstr
  } else if (!is.null(custom_label)) {
    custom_label
  } else {
    "counts"
  }
  y$unique <- formatters::with_label(
    s_num_patients_content(df = df, .N_col = 1, .var = id, required = NULL)$unique[1L],
    row_label
  )
  y$all <- formatters::with_label(
    nrow(df),
    row_label
  )
  events <- Map(
    function(filters) {
      formatters::with_label(
        s_count_patients_with_event(df = df, .var = ".row_index", filters = filters, .N_col = 1, .N_row = 1)$count,
        row_label
      )
    },
    filters = filters_list
  )
  y_complete <- c(y, events)
  y <- if (length(empty_stats) > 0) {
    y_reduced <- y_complete
    for (stat in intersect(names(y_complete), empty_stats)) {
      y_reduced[[stat]] <- formatters::with_label(character(), obj_label(y_reduced[[stat]]))
    }
    y_reduced
  } else {
    y_complete
  }
  y
}

#' @describeIn count_patients_events_in_cols Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @param col_split (`flag`)\cr whether the columns should be split.
#'   Set to `FALSE` when the required column split has been done already earlier in the layout pipe.
#'
#' @return
#' * `summarize_patients_events_in_cols()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
#'   containing the statistics from `s_count_patients_and_multiple_events()` to the table layout.
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = rep(c("id1", "id2", "id3", "id4"), c(2, 3, 1, 1)),
#'   ARM = c("A", "A", "B", "B", "B", "B", "A"),
#'   AESER = rep("Y", 7),
#'   AESDTH = c("Y", "Y", "N", "Y", "Y", "N", "N"),
#'   AEREL = c("Y", "Y", "N", "Y", "Y", "N", "Y"),
#'   AEDECOD = c("A", "A", "A", "B", "B", "C", "D"),
#'   AEBODSYS = rep(c("SOC1", "SOC2", "SOC3"), c(3, 3, 1))
#' )
#'
#' # `summarize_patients_events_in_cols()`
#' basic_table() %>%
#'   summarize_patients_events_in_cols(
#'     filters_list = list(
#'       related = formatters::with_label(c(AEREL = "Y"), "Events (Related)"),
#'       fatal = c(AESDTH = "Y"),
#'       fatal_related = c(AEREL = "Y", AESDTH = "Y")
#'     ),
#'     custom_label = "%s Total number of patients and events"
#'   ) %>%
#'   build_table(df)
#'
#' @export
#' @order 2
summarize_patients_events_in_cols <- function(lyt, # nolint
                                              id = "USUBJID",
                                              filters_list = list(),
                                              empty_stats = character(),
                                              na_str = default_na_str(),
                                              ...,
                                              .stats = c(
                                                "unique",
                                                "all",
                                                names(filters_list)
                                              ),
                                              .labels = c(
                                                unique = "Patients (All)",
                                                all = "Events (All)",
                                                labels_or_names(filters_list)
                                              ),
                                              col_split = TRUE) {
  extra_args <- list(id = id, filters_list = filters_list, empty_stats = empty_stats, ...)

  afun_list <- Map(
    function(stat) {
      make_afun(
        s_count_patients_and_multiple_events,
        .stats = stat,
        .formats = "xx."
      )
    },
    stat = .stats
  )
  if (col_split) {
    lyt <- split_cols_by_multivar(
      lyt = lyt,
      vars = rep(id, length(.stats)),
      varlabels = .labels[.stats]
    )
  }
  summarize_row_groups(
    lyt = lyt,
    cfun = afun_list,
    na_str = na_str,
    extra_args = extra_args
  )
}

#' Subgroup treatment effect pattern (STEP) fit for survival outcome
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This fits the subgroup treatment effect pattern (STEP) models for a survival outcome. The treatment arm
#' variable must have exactly 2 levels, where the first one is taken as reference and the estimated
#' hazard ratios are for the comparison of the second level vs. the first one.
#'
#' The model which is fit is:
#'
#' `Surv(time, event) ~ arm * poly(biomarker, degree) + covariates + strata(strata)`
#'
#' where `degree` is specified by `control_step()`.
#'
#' @inheritParams argument_convention
#' @param variables (named `list` of `character`)\cr list of analysis variables: needs `time`, `event`,
#'   `arm`, `biomarker`, and optional `covariates` and `strata`.
#' @param control (named `list`)\cr combined control list from [control_step()] and [control_coxph()].
#'
#' @return A matrix of class `step`. The first part of the columns describe the subgroup intervals used
#'   for the biomarker variable, including where the center of the intervals are and their bounds. The
#'   second part of the columns contain the estimates for the treatment arm comparison.
#'
#' @note For the default degree 0 the `biomarker` variable is not included in the model.
#'
#' @seealso [control_step()] and [control_coxph()] for the available customization options.
#'
#' @examples
#' # Testing dataset with just two treatment arms.
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(
#'     PARAMCD == "OS",
#'     ARM %in% c("B: Placebo", "A: Drug X")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to display reference arm before treatment arm.
#'     ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
#'     is_event = CNSR == 0
#'   )
#' labels <- c("ARM" = "Treatment Arm", "is_event" = "Event Flag")
#' formatters::var_labels(adtte_f)[names(labels)] <- labels
#'
#' variables <- list(
#'   arm = "ARM",
#'   biomarker = "BMRKR1",
#'   covariates = c("AGE", "BMRKR2"),
#'   event = "is_event",
#'   time = "AVAL"
#' )
#'
#' # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup.
#' step_matrix <- fit_survival_step(
#'   variables = variables,
#'   data = adtte_f
#' )
#' dim(step_matrix)
#' head(step_matrix)
#'
#' # Specify different polynomial degree for the biomarker interaction to use more flexible local
#' # models. Or specify different Cox regression options.
#' step_matrix2 <- fit_survival_step(
#'   variables = variables,
#'   data = adtte_f,
#'   control = c(control_coxph(conf_level = 0.9), control_step(degree = 2))
#' )
#'
#' # Use a global model with cubic interaction and only 5 points.
#' step_matrix3 <- fit_survival_step(
#'   variables = variables,
#'   data = adtte_f,
#'   control = c(control_coxph(), control_step(bandwidth = NULL, degree = 3, num_points = 5L))
#' )
#'
#' @export
fit_survival_step <- function(variables,
                              data,
                              control = c(control_step(), control_coxph())) {
  checkmate::assert_list(control)
  assert_df_with_variables(data, variables)
  data <- data[!is.na(data[[variables$biomarker]]), ]
  window_sel <- h_step_window(x = data[[variables$biomarker]], control = control)
  interval_center <- window_sel$interval[, "Interval Center"]
  form <- h_step_survival_formula(variables = variables, control = control)
  estimates <- if (is.null(control$bandwidth)) {
    h_step_survival_est(
      formula = form,
      data = data,
      variables = variables,
      x = interval_center,
      control = control
    )
  } else {
    tmp <- mapply(
      FUN = h_step_survival_est,
      x = interval_center,
      subset = as.list(as.data.frame(window_sel$sel)),
      MoreArgs = list(
        formula = form,
        data = data,
        variables = variables,
        control = control
      )
    )
    # Maybe we find a more elegant solution than this.
    rownames(tmp) <- c("n", "events", "loghr", "se", "ci_lower", "ci_upper")
    t(tmp)
  }
  result <- cbind(window_sel$interval, estimates)
  structure(
    result,
    class = c("step", "matrix"),
    variables = variables,
    control = control
  )
}

#' Count the number of patients with particular flags
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_patients_with_flags()] creates a layout element to calculate counts of patients for
#' which user-specified flags are present.
#'
#' This function analyzes primary analysis variable `var` which indicates unique subject identifiers. Flags
#' variables to analyze are specified by the user via the `flag_variables` argument, and must either take value
#' `TRUE` (flag present) or `FALSE` (flag absent) for each record.
#'
#' If there are multiple records with the same flag present for a patient, only one occurrence is counted.
#'
#' @inheritParams argument_convention
#' @param flag_variables (`character`)\cr a vector specifying the names of `logical` variables from analysis dataset
#'   used for counting the number of unique identifiers.
#' @param flag_labels (`character`)\cr vector of labels to use for flag variables. If any labels are also specified via
#'   the `.labels` parameter, the `.labels` values will take precedence and replace these labels.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("count_patients_with_flags"), type = "sh")``
#'
#' @seealso [count_patients_with_event]
#'
#' @name count_patients_with_flags
#' @order 1
NULL

#' @describeIn count_patients_with_flags Statistics function which counts the number of patients for which
#'   a particular flag variable is `TRUE`.
#'
#' @inheritParams analyze_variables
#' @param .var (`string`)\cr name of the column that contains the unique identifier.
#'
#' @note If `flag_labels` is not specified, variables labels will be extracted from `df`. If variables are not
#'   labeled, variable names will be used instead. Alternatively, a named `vector` can be supplied to
#'   `flag_variables` such that within each name-value pair the name corresponds to the variable name and the value is
#'   the label to use for this variable.
#'
#' @return
#' * `s_count_patients_with_flags()` returns the count and the fraction of unique identifiers with each particular
#'   flag as a list of statistics `n`, `count`, `count_fraction`, and `n_blq`, with one element per flag.
#'
#' @examples
#' # `s_count_patients_with_flags()`
#'
#' s_count_patients_with_flags(
#'   adae,
#'   "SUBJID",
#'   flag_variables = c("fl1", "fl2", "fl3", "fl4"),
#'   denom = "N_col",
#'   .N_col = 1000
#' )
#'
#' @export
s_count_patients_with_flags <- function(df,
                                        .var,
                                        flag_variables,
                                        flag_labels = NULL,
                                        .N_col = ncol(df), # nolint
                                        .N_row = nrow(df), # nolint
                                        denom = c("n", "N_col", "N_row")) {
  checkmate::assert_character(flag_variables)
  if (!is.null(flag_labels)) {
    checkmate::assert_character(flag_labels, len = length(flag_variables), any.missing = FALSE)
    flag_names <- flag_labels
  } else {
    if (is.null(names(flag_variables))) {
      flag_names <- formatters::var_labels(df[flag_variables], fill = TRUE)
    } else {
      flag_names <- unname(flag_variables)
      flag_variables <- names(flag_variables)
    }
  }

  checkmate::assert_subset(flag_variables, colnames(df))
  temp <- sapply(flag_variables, function(x) {
    tmp <- Map(function(y) which(df[[y]]), x)
    position_satisfy_flags <- Reduce(intersect, tmp)
    id_satisfy_flags <- as.character(unique(df[position_satisfy_flags, ][[.var]]))
    s_count_values(
      x = as.character(unique(df[[.var]])),
      values = id_satisfy_flags,
      denom = denom,
      .N_col = .N_col,
      .N_row = .N_row
    )
  })
  colnames(temp) <- flag_names
  temp <- data.frame(t(temp))
  result <- as.list(temp)
  if (length(flag_variables) == 1) {
    for (i in seq(3)) names(result[[i]]) <- flag_names[1]
  }
  result
}

#' @describeIn count_patients_with_flags Formatted analysis function which is used as `afun`
#'   in `count_patients_with_flags()`.
#'
#' @return
#' * `a_count_patients_with_flags()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @examples
#' a_count_patients_with_flags(
#'   adae,
#'   .N_col = 10L,
#'   .N_row = 10L,
#'   .var = "USUBJID",
#'   flag_variables = c("fl1", "fl2", "fl3", "fl4")
#' )
#'
#' @export
a_count_patients_with_flags <- function(df,
                                        labelstr = "",
                                        flag_variables,
                                        flag_labels = NULL,
                                        denom = c("n", "N_col", "N_row"),
                                        .N_col = ncol(df), # nolint
                                        .N_row = nrow(df), # nolint
                                        .df_row,
                                        .var = NULL,
                                        .stats = NULL,
                                        .stat_names = NULL,
                                        .formats = NULL,
                                        .labels = NULL,
                                        .indent_mods = NULL,
                                        na_str = default_na_str()) {
  x_stats <- s_count_patients_with_flags(
    df = df, .var = .var, flag_variables = flag_variables, flag_labels = flag_labels,
    .N_col = .N_col, .N_row = .N_row, denom = denom
  )
  if (is.null(names(flag_variables))) flag_variables <- formatters::var_labels(df, fill = TRUE)[flag_variables]
  if (is.null(flag_labels)) flag_labels <- flag_variables

  if (is.null(unlist(x_stats))) {
    return(NULL)
  }

  # Fill in with formatting defaults if needed
  .stats <- get_stats("count_patients_with_flags", stats_in = .stats)
  levels_per_stats <- rep(list(names(flag_variables)), length(.stats)) %>% setNames(.stats)
  .formats <- get_formats_from_stats(.stats, .formats, levels_per_stats)
  .indent_mods <- get_indents_from_stats(.stats, .indent_mods, levels_per_stats)
  .labels <- get_labels_from_stats(
    .stats, .labels, levels_per_stats,
    flag_labels %>% setNames(names(flag_variables))
  )

  x_stats <- x_stats[.stats]

  # Unlist stats
  x_stats <- x_stats %>%
    .unlist_keep_nulls() %>%
    setNames(names(.formats))

  # Auto format handling
  .formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)

  # Get and check statistical names from defaults
  .stat_names <- get_stat_names(x_stats, .stat_names)

  in_rows(
    .list = x_stats,
    .formats = .formats,
    .names = names(.labels),
    .stat_names = .stat_names,
    .labels = .labels %>% .unlist_keep_nulls(),
    .indent_mods = .indent_mods %>% .unlist_keep_nulls(),
    .format_na_strs = na_str
  )
}

#' @describeIn count_patients_with_flags Layout-creating function which can take statistics function
#'   arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_patients_with_flags()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_patients_with_flags()` to the table layout.
#'
#' @examples
#' # Add labelled flag variables to analysis dataset.
#' adae <- tern_ex_adae %>%
#'   dplyr::mutate(
#'     fl1 = TRUE %>% with_label("Total AEs"),
#'     fl2 = (TRTEMFL == "Y") %>%
#'       with_label("Total number of patients with at least one adverse event"),
#'     fl3 = (TRTEMFL == "Y" & AEOUT == "FATAL") %>%
#'       with_label("Total number of patients with fatal AEs"),
#'     fl4 = (TRTEMFL == "Y" & AEOUT == "FATAL" & AEREL == "Y") %>%
#'       with_label("Total number of patients with related fatal AEs")
#'   )
#'
#' lyt <- basic_table() %>%
#'   split_cols_by("ARM") %>%
#'   add_colcounts() %>%
#'   count_patients_with_flags(
#'     "SUBJID",
#'     flag_variables = c("fl1", "fl2", "fl3", "fl4"),
#'     denom = "N_col"
#'   )
#'
#' build_table(lyt, adae, alt_counts_df = tern_ex_adsl)
#'
#' @export
#' @order 2
count_patients_with_flags <- function(lyt,
                                      var,
                                      flag_variables,
                                      flag_labels = NULL,
                                      var_labels = var,
                                      show_labels = "hidden",
                                      riskdiff = FALSE,
                                      na_str = default_na_str(),
                                      nested = TRUE,
                                      ...,
                                      table_names = paste0("tbl_flags_", var),
                                      .stats = "count_fraction",
                                      .stat_names = NULL,
                                      .formats = list(count_fraction = format_count_fraction_fixed_dp),
                                      .indent_mods = NULL,
                                      .labels = NULL) {
  checkmate::assert_flag(riskdiff)
  extra_args <- list(
    .stats = .stats, .stat_names = .stat_names, .formats = .formats, .labels = .labels,
    .indent_mods = .indent_mods, na_str = na_str
  )
  s_args <- list(flag_variables = flag_variables, flag_labels = flag_labels, ...)

  if (isFALSE(riskdiff)) {
    extra_args <- c(extra_args, s_args)
  } else {
    extra_args <- c(
      extra_args,
      list(
        afun = list("s_count_patients_with_flags" = a_count_patients_with_flags),
        s_args = s_args
      )
    )
  }

  analyze(
    lyt = lyt,
    vars = var,
    afun = ifelse(isFALSE(riskdiff), a_count_patients_with_flags, afun_riskdiff),
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Analyze a pairwise Cox-PH model
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [coxph_pairwise()] creates a layout element to analyze a pairwise Cox-PH model.
#'
#' This function can return statistics including p-value, hazard ratio (HR), and HR confidence intervals from both
#' stratified and unstratified Cox-PH models. The variable(s) to be analyzed is specified via the `vars` argument and
#' any stratification factors via the `strata` argument.
#'
#' @inheritParams argument_convention
#' @inheritParams s_surv_time
#' @param strata (`character` or `NULL`)\cr variable names indicating stratification factors.
#' @param strat `r lifecycle::badge("deprecated")` Please use the `strata` argument instead.
#' @param control (`list`)\cr parameters for comparison details, specified by using the helper function
#'   [control_coxph()]. Some possible parameter options are:
#'   * `pval_method` (`string`)\cr p-value method for testing the null hypothesis that hazard ratio = 1. Default
#'     method is `"log-rank"` which comes from [survival::survdiff()], can also be set to `"wald"` or `"likelihood"`
#'     (from [survival::coxph()]).
#'   * `ties` (`string`)\cr specifying the method for tie handling. Default is `"efron"`,
#'     can also be set to `"breslow"` or `"exact"`. See more in [survival::coxph()].
#'   * `conf_level` (`proportion`)\cr confidence level of the interval for HR.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("coxph_pairwise"), type = "sh")``
#'
#' @name survival_coxph_pairwise
#' @order 1
NULL

#' @describeIn survival_coxph_pairwise Statistics function which analyzes HR, CIs of HR, and p-value of a Cox-PH model.
#'
#' @return
#' * `s_coxph_pairwise()` returns the statistics:
#'   * `pvalue`: p-value to test the null hypothesis that hazard ratio = 1.
#'   * `hr`: Hazard ratio.
#'   * `hr_ci`: Confidence interval for hazard ratio.
#'   * `n_tot`: Total number of observations.
#'   * `n_tot_events`: Total number of events.
#'
#' @keywords internal
s_coxph_pairwise <- function(df,
                             .ref_group,
                             .in_ref_col,
                             .var,
                             is_event,
                             strata = NULL,
                             strat = lifecycle::deprecated(),
                             control = control_coxph()) {
  if (lifecycle::is_present(strat)) {
    lifecycle::deprecate_warn("0.9.4", "s_coxph_pairwise(strat)", "s_coxph_pairwise(strata)")
    strata <- strat
  }

  checkmate::assert_string(.var)
  checkmate::assert_numeric(df[[.var]])
  checkmate::assert_logical(df[[is_event]])
  assert_df_with_variables(df, list(tte = .var, is_event = is_event))
  pval_method <- control$pval_method
  ties <- control$ties
  conf_level <- control$conf_level

  if (.in_ref_col) {
    return(
      list(
        pvalue = formatters::with_label("", paste0("p-value (", pval_method, ")")),
        hr = formatters::with_label("", "Hazard Ratio"),
        hr_ci = formatters::with_label("", f_conf_level(conf_level)),
        hr_ci_3d = formatters::with_label("", paste0("Hazard Ratio (", f_conf_level(conf_level), ")")),
        n_tot = formatters::with_label("", "Total n"),
        n_tot_events = formatters::with_label("", "Total events")
      )
    )
  }
  data <- rbind(.ref_group, df)
  group <- factor(rep(c("ref", "x"), c(nrow(.ref_group), nrow(df))), levels = c("ref", "x"))

  df_cox <- data.frame(
    tte = data[[.var]],
    is_event = data[[is_event]],
    arm = group
  )
  if (is.null(strata)) {
    formula_cox <- survival::Surv(tte, is_event) ~ arm
  } else {
    formula_cox <- stats::as.formula(
      paste0(
        "survival::Surv(tte, is_event) ~ arm + strata(",
        paste(strata, collapse = ","),
        ")"
      )
    )
    df_cox <- cbind(df_cox, data[strata])
  }
  cox_fit <- survival::coxph(
    formula = formula_cox,
    data = df_cox,
    ties = ties
  )
  sum_cox <- summary(cox_fit, conf.int = conf_level, extend = TRUE)
  orginal_survdiff <- survival::survdiff(
    formula_cox,
    data = df_cox
  )
  log_rank_pvalue <- 1 - pchisq(orginal_survdiff$chisq, length(orginal_survdiff$n) - 1)

  pval <- switch(pval_method,
    "wald" = sum_cox$waldtest["pvalue"],
    "log-rank" = log_rank_pvalue, # pvalue from original log-rank test survival::survdiff()
    "likelihood" = sum_cox$logtest["pvalue"]
  )
  list(
    pvalue = formatters::with_label(unname(pval), paste0("p-value (", pval_method, ")")),
    hr = formatters::with_label(sum_cox$conf.int[1, 1], "Hazard Ratio"),
    hr_ci = formatters::with_label(unname(sum_cox$conf.int[1, 3:4]), f_conf_level(conf_level)),
    hr_ci_3d = formatters::with_label(
      c(sum_cox$conf.int[1, 1], unname(sum_cox$conf.int[1, 3:4])),
      paste0("Hazard Ratio (", f_conf_level(conf_level), ")")
    ),
    n_tot = formatters::with_label(sum_cox$n, "Total n"),
    n_tot_events = formatters::with_label(sum_cox$nevent, "Total events")
  )
}

#' @describeIn survival_coxph_pairwise Formatted analysis function which is used as `afun` in `coxph_pairwise()`.
#'
#' @return
#' * `a_coxph_pairwise()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_coxph_pairwise <- make_afun(
  s_coxph_pairwise,
  .indent_mods = c(pvalue = 0L, hr = 0L, hr_ci = 1L, n_tot = 0L, n_tot_events = 0L, hr_ci_3d = 0L),
  .formats = c(
    pvalue = "x.xxxx | (<0.0001)",
    hr = "xx.xx",
    hr_ci = "(xx.xx, xx.xx)",
    hr_ci_3d = "xx.xx (xx.xx - xx.xx)",
    n_tot = "xx.xx",
    n_tot_events = "xx.xx"
  )
)

#' @describeIn survival_coxph_pairwise Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `coxph_pairwise()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_coxph_pairwise()` to the table layout.
#'
#' @examples
#' library(dplyr)
#'
#' adtte_f <- tern_ex_adtte %>%
#'   filter(PARAMCD == "OS") %>%
#'   mutate(is_event = CNSR == 0)
#'
#' df <- adtte_f %>% filter(ARMCD == "ARM A")
#' df_ref_group <- adtte_f %>% filter(ARMCD == "ARM B")
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   coxph_pairwise(
#'     vars = "AVAL",
#'     is_event = "is_event",
#'     var_labels = "Unstratified Analysis"
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' basic_table() %>%
#'   split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
#'   add_colcounts() %>%
#'   coxph_pairwise(
#'     vars = "AVAL",
#'     is_event = "is_event",
#'     var_labels = "Stratified Analysis",
#'     strata = "SEX",
#'     control = control_coxph(pval_method = "wald")
#'   ) %>%
#'   build_table(df = adtte_f)
#'
#' @export
#' @order 2
coxph_pairwise <- function(lyt,
                           vars,
                           strata = NULL,
                           control = control_coxph(),
                           na_str = default_na_str(),
                           nested = TRUE,
                           ...,
                           var_labels = "CoxPH",
                           show_labels = "visible",
                           table_names = vars,
                           .stats = c("pvalue", "hr", "hr_ci"),
                           .formats = NULL,
                           .labels = NULL,
                           .indent_mods = NULL) {
  extra_args <- list(strata = strata, control = control, ...)

  afun <- make_afun(
    a_coxph_pairwise,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )
  analyze(
    lyt,
    vars,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Count patients with abnormal analysis range values by baseline status
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [count_abnormal_by_baseline()] creates a layout element to count patients with abnormal
#' analysis range values, categorized by baseline status.
#'
#' This function analyzes primary analysis variable `var` which indicates abnormal range results. Additional
#' analysis variables that can be supplied as a list via the `variables` parameter are `id` (defaults to
#' `USUBJID`), a variable to indicate unique subject identifiers, and `baseline` (defaults to `BNRIND`), a
#' variable to indicate baseline reference ranges.
#'
#' For each direction specified via the `abnormal` parameter (e.g. High or Low), we condition on baseline
#' range result and count patients in the numerator and denominator as follows for each of the following
#' categories:
#'   * `Not <abnormality>`
#'     * `num`:  The number of patients without abnormality at baseline (excluding those with missing baseline)
#'       and with at least one abnormality post-baseline.
#'     * `denom`: The number of patients without abnormality at baseline (excluding those with missing baseline).
#'   * `<Abnormality>`
#'     * `num`: The number of patients with abnormality as baseline and at least one abnormality post-baseline.
#'     * `denom`: The number of patients with abnormality at baseline.
#'   * `Total`
#'     * `num`: The number of patients with at least one post-baseline record and at least one abnormality
#'       post-baseline.
#'     * `denom`: The number of patients with at least one post-baseline record.
#'
#' This function assumes that `df` has been filtered to only include post-baseline records.
#'
#' @inheritParams argument_convention
#' @param abnormal (`character`)\cr values identifying the abnormal range level(s) in `.var`.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("abnormal_by_baseline"), type = "sh")``
#'
#' @note
#' * `df` should be filtered to include only post-baseline records.
#' * If the baseline variable or analysis variable contains `NA` records, it is expected that `df` has been
#'   pre-processed using [df_explicit_na()] or [explicit_na()].
#'
#' @seealso Relevant description function [d_count_abnormal_by_baseline()].
#'
#' @name abnormal_by_baseline
#' @order 1
NULL

#' Description function for `s_count_abnormal_by_baseline()`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Description function that produces the labels for [s_count_abnormal_by_baseline()].
#'
#' @inheritParams abnormal_by_baseline
#'
#' @return Abnormal category labels for [s_count_abnormal_by_baseline()].
#'
#' @examples
#' d_count_abnormal_by_baseline("LOW")
#'
#' @export
d_count_abnormal_by_baseline <- function(abnormal) {
  not_abn_name <- paste("Not", tolower(abnormal))
  abn_name <- paste0(toupper(substr(abnormal, 1, 1)), tolower(substring(abnormal, 2)))
  total_name <- "Total"

  list(
    not_abnormal = not_abn_name,
    abnormal = abn_name,
    total = total_name
  )
}

#' @describeIn abnormal_by_baseline Statistics function for a single `abnormal` level.
#'
#' @param na_str (`string`)\cr the explicit `na_level` argument you used in the pre-processing steps (maybe with
#'   [df_explicit_na()]). The default is `"<Missing>"`.
#'
#' @return
#' * `s_count_abnormal_by_baseline()` returns statistic `fraction` which is a named list with 3 labeled elements:
#'   `not_abnormal`, `abnormal`, and `total`. Each element contains a vector with `num` and `denom` patient counts.
#'
#' @keywords internal
s_count_abnormal_by_baseline <- function(df,
                                         .var,
                                         abnormal,
                                         na_str = "<Missing>",
                                         variables = list(id = "USUBJID", baseline = "BNRIND")) {
  checkmate::assert_string(.var)
  checkmate::assert_string(abnormal)
  checkmate::assert_string(na_str)
  assert_df_with_variables(df, c(range = .var, variables))
  checkmate::assert_subset(names(variables), c("id", "baseline"))
  checkmate::assert_multi_class(df[[variables$id]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[variables$baseline]], classes = c("factor", "character"))
  checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))

  # If input is passed as character, changed to factor
  df[[.var]] <- as_factor_keep_attributes(df[[.var]], na_level = na_str)
  df[[variables$baseline]] <- as_factor_keep_attributes(df[[variables$baseline]], na_level = na_str)

  assert_valid_factor(df[[.var]], any.missing = FALSE)
  assert_valid_factor(df[[variables$baseline]], any.missing = FALSE)

  # Keep only records with valid analysis value.
  df <- df[df[[.var]] != na_str, ]

  anl <- data.frame(
    id = df[[variables$id]],
    var = df[[.var]],
    baseline = df[[variables$baseline]],
    stringsAsFactors = FALSE
  )

  # Total:
  #  - Patients in denominator: have at least one valid measurement post-baseline.
  #  - Patients in numerator: have at least one abnormality.
  total_denom <- length(unique(anl$id))
  total_num <- length(unique(anl$id[anl$var == abnormal]))

  # Baseline NA records are counted only in total rows.
  anl <- anl[anl$baseline != na_str, ]

  # Abnormal:
  #   - Patients in denominator: have abnormality at baseline.
  #   - Patients in numerator: have abnormality at baseline AND
  #     have at least one abnormality post-baseline.
  abn_denom <- length(unique(anl$id[anl$baseline == abnormal]))
  abn_num <- length(unique(anl$id[anl$baseline == abnormal & anl$var == abnormal]))

  # Not abnormal:
  #   - Patients in denominator: do not have abnormality at baseline.
  #   - Patients in numerator: do not have abnormality at baseline AND
  #     have at least one abnormality post-baseline.
  not_abn_denom <- length(unique(anl$id[anl$baseline != abnormal]))
  not_abn_num <- length(unique(anl$id[anl$baseline != abnormal & anl$var == abnormal]))

  labels <- d_count_abnormal_by_baseline(abnormal)
  list(fraction = list(
    not_abnormal = formatters::with_label(c(num = not_abn_num, denom = not_abn_denom), labels$not_abnormal),
    abnormal = formatters::with_label(c(num = abn_num, denom = abn_denom), labels$abnormal),
    total = formatters::with_label(c(num = total_num, denom = total_denom), labels$total)
  ))
}

#' @describeIn abnormal_by_baseline Formatted analysis function which is used as `afun`
#'   in `count_abnormal_by_baseline()`.
#'
#' @return
#' * `a_count_abnormal_by_baseline()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_count_abnormal_by_baseline <- make_afun(
  s_count_abnormal_by_baseline,
  .formats = c(fraction = format_fraction)
)

#' @describeIn abnormal_by_baseline Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `count_abnormal_by_baseline()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_count_abnormal_by_baseline()` to the table layout.
#'
#' @examples
#' df <- data.frame(
#'   USUBJID = as.character(c(1:6)),
#'   ANRIND = factor(c(rep("LOW", 4), "NORMAL", "HIGH")),
#'   BNRIND = factor(c("LOW", "NORMAL", "HIGH", NA, "LOW", "NORMAL"))
#' )
#' df <- df_explicit_na(df)
#'
#' # Layout creating function.
#' basic_table() %>%
#'   count_abnormal_by_baseline(var = "ANRIND", abnormal = c(High = "HIGH")) %>%
#'   build_table(df)
#'
#' # Passing of statistics function and formatting arguments.
#' df2 <- data.frame(
#'   ID = as.character(c(1, 2, 3, 4)),
#'   RANGE = factor(c("NORMAL", "LOW", "HIGH", "HIGH")),
#'   BLRANGE = factor(c("LOW", "HIGH", "HIGH", "NORMAL"))
#' )
#'
#' basic_table() %>%
#'   count_abnormal_by_baseline(
#'     var = "RANGE",
#'     abnormal = c(Low = "LOW"),
#'     variables = list(id = "ID", baseline = "BLRANGE"),
#'     .formats = c(fraction = "xx / xx"),
#'     .indent_mods = c(fraction = 2L)
#'   ) %>%
#'   build_table(df2)
#'
#' @export
#' @order 2
count_abnormal_by_baseline <- function(lyt,
                                       var,
                                       abnormal,
                                       variables = list(id = "USUBJID", baseline = "BNRIND"),
                                       na_str = "<Missing>",
                                       nested = TRUE,
                                       ...,
                                       table_names = abnormal,
                                       .stats = NULL,
                                       .formats = NULL,
                                       .labels = NULL,
                                       .indent_mods = NULL) {
  checkmate::assert_character(abnormal, len = length(table_names), names = "named")
  checkmate::assert_string(var)

  extra_args <- list(abnormal = abnormal, variables = variables, na_str = na_str, ...)

  afun <- make_afun(
    a_count_abnormal_by_baseline,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods,
    .ungroup_stats = "fraction"
  )
  for (i in seq_along(abnormal)) {
    extra_args[["abnormal"]] <- abnormal[i]

    lyt <- analyze(
      lyt = lyt,
      vars = var,
      var_labels = names(abnormal[i]),
      afun = afun,
      na_str = na_str,
      nested = nested,
      table_names = table_names[i],
      extra_args = extra_args,
      show_labels = "visible"
    )
  }
  lyt
}

#' Sort pharmacokinetic data by `PARAM` variable
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @param pk_data (`data.frame`)\cr pharmacokinetic data frame.
#' @param key_var (`string`)\cr key variable used to merge pk_data and metadata created by [d_pkparam()].
#'
#' @return A pharmacokinetic `data.frame` sorted by a `PARAM` variable.
#'
#' @examples
#' library(dplyr)
#'
#' adpp <- tern_ex_adpp %>% mutate(PKPARAM = factor(paste0(PARAM, " (", AVALU, ")")))
#' pk_ordered_data <- h_pkparam_sort(adpp)
#'
#' @export
h_pkparam_sort <- function(pk_data, key_var = "PARAMCD") {
  assert_df_with_variables(pk_data, list(key_var = key_var))
  pk_data$PARAMCD <- pk_data[[key_var]]

  ordered_pk_data <- d_pkparam()

  # Add the numeric values from ordered_pk_data to pk_data
  joined_data <- merge(pk_data, ordered_pk_data, by = "PARAMCD", suffixes = c("", ".y"))

  joined_data <- joined_data[, -grep(".*.y$", colnames(joined_data))]

  joined_data$TLG_ORDER <- as.numeric(joined_data$TLG_ORDER)

  # Then order PARAM based on this column
  joined_data$PARAM <- factor(joined_data$PARAM,
    levels = unique(joined_data$PARAM[order(joined_data$TLG_ORDER)]),
    ordered = TRUE
  )

  joined_data$TLG_DISPLAY <- factor(joined_data$TLG_DISPLAY,
    levels = unique(joined_data$TLG_DISPLAY[order(joined_data$TLG_ORDER)]),
    ordered = TRUE
  )

  joined_data
}

#' Summarize analysis of covariance (ANCOVA) results
#'
#' @description `r lifecycle::badge("stable")`
#'
#' The analyze function [summarize_ancova()] creates a layout element to summarize ANCOVA results.
#'
#' This function can be used to analyze multiple endpoints and/or multiple timepoints within the response variable(s)
#' specified as `vars`.
#'
#' Additional variables for the analysis, namely an arm (grouping) variable and covariate variables, can be defined
#' via the `variables` argument. See below for more details on how to specify `variables`. An interaction term can
#' be implemented in the model if needed. The interaction variable that should interact with the arm variable is
#' specified via the `interaction_term` parameter, and the specific value of `interaction_term` for which to extract
#' the ANCOVA results via the `interaction_y` parameter.
#'
#' @inheritParams h_ancova
#' @inheritParams argument_convention
#' @param interaction_y (`string` or `flag`)\cr a selected item inside of the `interaction_item` variable which will be
#'   used to select the specific ANCOVA results. if the interaction is not needed, the default option is `FALSE`.
#' @param .stats (`character`)\cr statistics to select for the table.
#'
#'   Options are: ``r shQuote(get_stats("summarize_ancova"), type = "sh")``
#'
#' @name summarize_ancova
#' @order 1
NULL

#' Helper function to return results of a linear model
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @inheritParams argument_convention
#' @param .df_row (`data.frame`)\cr data set that includes all the variables that are called in `.var` and `variables`.
#' @param variables (named `list` of `string`)\cr list of additional analysis variables, with expected elements:
#'   * `arm` (`string`)\cr group variable, for which the covariate adjusted means of multiple groups will be
#'     summarized. Specifically, the first level of `arm` variable is taken as the reference group.
#'   * `covariates` (`character`)\cr a vector that can contain single variable names (such as `"X1"`), and/or
#'     interaction terms indicated by `"X1 * X2"`.
#' @param interaction_item (`string` or `NULL`)\cr name of the variable that should have interactions
#'   with arm. if the interaction is not needed, the default option is `NULL`.
#'
#' @return The summary of a linear model.
#'
#' @examples
#' h_ancova(
#'   .var = "Sepal.Length",
#'   .df_row = iris,
#'   variables = list(arm = "Species", covariates = c("Petal.Length * Petal.Width", "Sepal.Width"))
#' )
#'
#' @export
h_ancova <- function(.var,
                     .df_row,
                     variables,
                     interaction_item = NULL) {
  checkmate::assert_string(.var)
  checkmate::assert_list(variables)
  checkmate::assert_subset(names(variables), c("arm", "covariates"))
  assert_df_with_variables(.df_row, list(rsp = .var))

  arm <- variables$arm
  covariates <- variables$covariates
  if (!is.null(covariates) && length(covariates) > 0) {
    # Get all covariate variable names in the model.
    var_list <- get_covariates(covariates)
    assert_df_with_variables(.df_row, var_list)
  }

  covariates_part <- paste(covariates, collapse = " + ")
  if (covariates_part != "") {
    formula <- stats::as.formula(paste0(.var, " ~ ", covariates_part, " + ", arm))
  } else {
    formula <- stats::as.formula(paste0(.var, " ~ ", arm))
  }

  if (is.null(interaction_item)) {
    specs <- arm
  } else {
    specs <- c(arm, interaction_item)
  }

  lm_fit <- stats::lm(
    formula = formula,
    data = .df_row
  )
  emmeans_fit <- emmeans::emmeans(
    lm_fit,
    # Specify here the group variable over which EMM are desired.
    specs = specs,
    # Pass the data again so that the factor levels of the arm variable can be inferred.
    data = .df_row
  )

  emmeans_fit
}

#' @describeIn summarize_ancova Statistics function that produces a named list of results
#'   of the investigated linear model.
#'
#' @return
#' * `s_ancova()` returns a named list of 5 statistics:
#'   * `n`: Count of complete sample size for the group.
#'   * `lsmean`: Estimated marginal means in the group.
#'   * `lsmean_diff`: Difference in estimated marginal means in comparison to the reference group.
#'     If working with the reference group, this will be empty.
#'   * `lsmean_diff_ci`: Confidence level for difference in estimated marginal means in comparison
#'     to the reference group.
#'   * `pval`: p-value (not adjusted for multiple comparisons).
#'
#' @keywords internal
s_ancova <- function(df,
                     .var,
                     .df_row,
                     variables,
                     .ref_group,
                     .in_ref_col,
                     conf_level,
                     interaction_y = FALSE,
                     interaction_item = NULL) {
  emmeans_fit <- h_ancova(.var = .var, variables = variables, .df_row = .df_row, interaction_item = interaction_item)

  sum_fit <- summary(
    emmeans_fit,
    level = conf_level
  )

  arm <- variables$arm

  sum_level <- as.character(unique(df[[arm]]))

  # Ensure that there is only one element in sum_level.
  checkmate::assert_scalar(sum_level)

  sum_fit_level <- sum_fit[sum_fit[[arm]] == sum_level, ]

  # Get the index of the ref arm
  if (interaction_y != FALSE) {
    y <- unlist(df[(df[[interaction_item]] == interaction_y), .var])
    # convert characters selected in interaction_y into the numeric order
    interaction_y <- which(sum_fit_level[[interaction_item]] == interaction_y)
    sum_fit_level <- sum_fit_level[interaction_y, ]
    # if interaction is called, reset the index
    ref_key <- seq(sum_fit[[arm]][unique(.ref_group[[arm]])])
    ref_key <- tail(ref_key, n = 1)
    ref_key <- (interaction_y - 1) * length(unique(.df_row[[arm]])) + ref_key
  } else {
    y <- df[[.var]]
    # Get the index of the ref arm when interaction is not called
    ref_key <- seq(sum_fit[[arm]][unique(.ref_group[[arm]])])
    ref_key <- tail(ref_key, n = 1)
  }

  if (.in_ref_col) {
    list(
      n = length(y[!is.na(y)]),
      lsmean = formatters::with_label(sum_fit_level$emmean, "Adjusted Mean"),
      lsmean_diff = formatters::with_label(character(), "Difference in Adjusted Means"),
      lsmean_diff_ci = formatters::with_label(character(), f_conf_level(conf_level)),
      pval = formatters::with_label(character(), "p-value")
    )
  } else {
    # Estimate the differences between the marginal means.
    emmeans_contrasts <- emmeans::contrast(
      emmeans_fit,
      # Compare all arms versus the control arm.
      method = "trt.vs.ctrl",
      # Take the arm factor from .ref_group as the control arm.
      ref = ref_key,
      level = conf_level
    )
    sum_contrasts <- summary(
      emmeans_contrasts,
      # Derive confidence intervals, t-tests and p-values.
      infer = TRUE,
      # Do not adjust the p-values for multiplicity.
      adjust = "none"
    )

    contrast_lvls <- gsub(
      "^\\(|\\)$", "", gsub(paste0(" - \\(*", .ref_group[[arm]][1], ".*"), "", sum_contrasts$contrast)
    )
    if (!is.null(interaction_item)) {
      sum_contrasts_level <- sum_contrasts[grepl(sum_level, contrast_lvls, fixed = TRUE), ]
    } else {
      sum_contrasts_level <- sum_contrasts[sum_level == contrast_lvls, ]
    }
    if (interaction_y != FALSE) {
      sum_contrasts_level <- sum_contrasts_level[interaction_y, ]
    }

    list(
      n = length(y[!is.na(y)]),
      lsmean = formatters::with_label(sum_fit_level$emmean, "Adjusted Mean"),
      lsmean_diff = formatters::with_label(sum_contrasts_level$estimate, "Difference in Adjusted Means"),
      lsmean_diff_ci = formatters::with_label(
        c(sum_contrasts_level$lower.CL, sum_contrasts_level$upper.CL),
        f_conf_level(conf_level)
      ),
      pval = formatters::with_label(sum_contrasts_level$p.value, "p-value")
    )
  }
}

#' @describeIn summarize_ancova Formatted analysis function which is used as `afun` in `summarize_ancova()`.
#'
#' @return
#' * `a_ancova()` returns the corresponding list with formatted [rtables::CellValue()].
#'
#' @keywords internal
a_ancova <- make_afun(
  s_ancova,
  .indent_mods = c("n" = 0L, "lsmean" = 0L, "lsmean_diff" = 0L, "lsmean_diff_ci" = 1L, "pval" = 1L),
  .formats = c(
    "n" = "xx",
    "lsmean" = "xx.xx",
    "lsmean_diff" = "xx.xx",
    "lsmean_diff_ci" = "(xx.xx, xx.xx)",
    "pval" = "x.xxxx | (<0.0001)"
  ),
  .null_ref_cells = FALSE
)

#' @describeIn summarize_ancova Layout-creating function which can take statistics function arguments
#'   and additional format arguments. This function is a wrapper for [rtables::analyze()].
#'
#' @return
#' * `summarize_ancova()` returns a layout object suitable for passing to further layouting functions,
#'   or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
#'   the statistics from `s_ancova()` to the table layout.
#'
#' @examples
#' basic_table() %>%
#'   split_cols_by("Species", ref_group = "setosa") %>%
#'   add_colcounts() %>%
#'   summarize_ancova(
#'     vars = "Petal.Length",
#'     variables = list(arm = "Species", covariates = NULL),
#'     table_names = "unadj",
#'     conf_level = 0.95, var_labels = "Unadjusted comparison",
#'     .labels = c(lsmean = "Mean", lsmean_diff = "Difference in Means")
#'   ) %>%
#'   summarize_ancova(
#'     vars = "Petal.Length",
#'     variables = list(arm = "Species", covariates = c("Sepal.Length", "Sepal.Width")),
#'     table_names = "adj",
#'     conf_level = 0.95, var_labels = "Adjusted comparison (covariates: Sepal.Length and Sepal.Width)"
#'   ) %>%
#'   build_table(iris)
#'
#' @export
#' @order 2
summarize_ancova <- function(lyt,
                             vars,
                             variables,
                             conf_level,
                             interaction_y = FALSE,
                             interaction_item = NULL,
                             var_labels,
                             na_str = default_na_str(),
                             nested = TRUE,
                             ...,
                             show_labels = "visible",
                             table_names = vars,
                             .stats = NULL,
                             .formats = NULL,
                             .labels = NULL,
                             .indent_mods = NULL) {
  extra_args <- list(
    variables = variables, conf_level = conf_level, interaction_y = interaction_y,
    interaction_item = interaction_item, ...
  )

  afun <- make_afun(
    a_ancova,
    interaction_y = interaction_y,
    interaction_item = interaction_item,
    .stats = .stats,
    .formats = .formats,
    .labels = .labels,
    .indent_mods = .indent_mods
  )

  analyze(
    lyt,
    vars,
    var_labels = var_labels,
    show_labels = show_labels,
    table_names = table_names,
    afun = afun,
    na_str = na_str,
    nested = nested,
    extra_args = extra_args
  )
}

#' Control function for incidence rate
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for the incidence rate, used
#' internally to specify details in `s_incidence_rate()`.
#'
#' @inheritParams argument_convention
#' @param conf_type (`string`)\cr `normal` (default), `normal_log`, `exact`, or `byar`
#'   for confidence interval type.
#' @param input_time_unit (`string`)\cr `day`, `week`, `month`, or `year` (default)
#'   indicating time unit for data input.
#' @param num_pt_year (`numeric(1)`)\cr number of patient-years to use when calculating adverse event rates.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @seealso [incidence_rate]
#'
#' @examples
#' control_incidence_rate(0.9, "exact", "month", 100)
#'
#' @export
control_incidence_rate <- function(conf_level = 0.95,
                                   conf_type = c("normal", "normal_log", "exact", "byar"),
                                   input_time_unit = c("year", "day", "week", "month"),
                                   num_pt_year = 100) {
  conf_type <- match.arg(conf_type)
  input_time_unit <- match.arg(input_time_unit)
  checkmate::assert_number(num_pt_year)
  assert_proportion_value(conf_level)

  list(
    conf_level = conf_level,
    conf_type = conf_type,
    input_time_unit = input_time_unit,
    num_pt_year = num_pt_year
  )
}

#' Helper function for tabulation of a single biomarker result
#'
#' @description `r lifecycle::badge("stable")`
#'
#' Please see [h_tab_surv_one_biomarker()] and [h_tab_rsp_one_biomarker()], which use this function for examples.
#' This function is a wrapper for [rtables::summarize_row_groups()].
#'
#' @inheritParams argument_convention
#' @param df (`data.frame`)\cr results for a single biomarker.
#' @param afuns (named `list` of `function`)\cr analysis functions.
#' @param colvars (named `list`)\cr named list with elements `vars` (variables to tabulate) and `labels` (their labels).
#'
#' @return An `rtables` table object with statistics in columns.
#'
#' @export
h_tab_one_biomarker <- function(df,
                                afuns,
                                colvars,
                                na_str = default_na_str(),
                                .indent_mods = 0L,
                                ...) {
  extra_args <- list(...)

  # Create "ci" column from "lcl" and "ucl"
  df$ci <- combine_vectors(df$lcl, df$ucl)

  lyt <- basic_table()

  # Row split by row type - only keep the content rows here.
  lyt <- split_rows_by(
    lyt = lyt,
    var = "row_type",
    split_fun = keep_split_levels("content"),
    nested = FALSE
  )

  # Summarize rows with all patients.
  lyt <- summarize_row_groups(
    lyt = lyt,
    var = "var_label",
    cfun = afuns,
    na_str = na_str,
    indent_mod = .indent_mods,
    extra_args = extra_args
  )

  # Split cols by the multiple variables to populate into columns.
  lyt <- split_cols_by_multivar(
    lyt = lyt,
    vars = colvars$vars,
    varlabels = colvars$labels
  )

  # If there is any subgroup variables, we extend the layout accordingly.
  if ("analysis" %in% df$row_type) {
    # Now only continue with the subgroup rows.
    lyt <- split_rows_by(
      lyt = lyt,
      var = "row_type",
      split_fun = keep_split_levels("analysis"),
      nested = FALSE,
      child_labels = "hidden"
    )

    # Split by the subgroup variable.
    lyt <- split_rows_by(
      lyt = lyt,
      var = "var",
      labels_var = "var_label",
      nested = TRUE,
      child_labels = "visible",
      indent_mod = .indent_mods * 2
    )

    # Then analyze colvars for each subgroup.
    lyt <- summarize_row_groups(
      lyt = lyt,
      cfun = afuns,
      var = "subgroup",
      na_str = na_str,
      extra_args = extra_args
    )
  }
  build_table(lyt, df = df)
}

#' Class for `CombinationFunction`
#'
#' @description `r lifecycle::badge("stable")`
#'
#' `CombinationFunction` is an S4 class which extends standard functions. These are special functions that
#' can be combined and negated with the logical operators.
#'
#' @param e1 (`CombinationFunction`)\cr left hand side of logical operator.
#' @param e2 (`CombinationFunction`)\cr right hand side of logical operator.
#' @param x (`CombinationFunction`)\cr the function which should be negated.
#'
#' @return A logical value indicating whether the left hand side of the equation equals the right hand side.
#'
#' @examples
#' higher <- function(a) {
#'   force(a)
#'   CombinationFunction(
#'     function(x) {
#'       x > a
#'     }
#'   )
#' }
#'
#' lower <- function(b) {
#'   force(b)
#'   CombinationFunction(
#'     function(x) {
#'       x < b
#'     }
#'   )
#' }
#'
#' c1 <- higher(5)
#' c2 <- lower(10)
#' c3 <- higher(5) & lower(10)
#' c3(7)
#'
#' @name combination_function
#' @aliases CombinationFunction-class
#' @exportClass CombinationFunction
#' @export CombinationFunction
CombinationFunction <- methods::setClass("CombinationFunction", contains = "function") # nolint

#' @describeIn combination_function Logical "AND" combination of `CombinationFunction` functions.
#'   The resulting object is of the same class, and evaluates the two argument functions. The result
#'   is then the "AND" of the two individual results.
#'
#' @export
methods::setMethod(
  "&",
  signature = c(e1 = "CombinationFunction", e2 = "CombinationFunction"),
  definition = function(e1, e2) {
    CombinationFunction(function(...) {
      e1(...) && e2(...)
    })
  }
)

#' @describeIn combination_function Logical "OR" combination of `CombinationFunction` functions.
#'   The resulting object is of the same class, and evaluates the two argument functions. The result
#'   is then the "OR" of the two individual results.
#'
#' @export
methods::setMethod(
  "|",
  signature = c(e1 = "CombinationFunction", e2 = "CombinationFunction"),
  definition = function(e1, e2) {
    CombinationFunction(function(...) {
      e1(...) || e2(...)
    })
  }
)

#' @describeIn combination_function Logical negation of `CombinationFunction` functions.
#'   The resulting object is of the same class, and evaluates the original function. The result
#'   is then the opposite of this results.
#'
#' @export
methods::setMethod(
  "!",
  signature = c(x = "CombinationFunction"),
  definition = function(x) {
    CombinationFunction(function(...) {
      !x(...)
    })
  }
)

#' Generate PK reference dataset
#'
#' @description `r lifecycle::badge("stable")`
#'
#' @return A `data.frame` of PK parameters.
#'
#' @examples
#' pk_reference_dataset <- d_pkparam()
#'
#' @export
d_pkparam <- function() {
  pk_dataset <- as.data.frame(matrix(
    c(
      "TMAX", "Time of CMAX", "Tmax", "Plasma/Blood/Serum", "1",
      "CMAX", "Max Conc", "Cmax", "Plasma/Blood/Serum", "2",
      "CMAXD", "Max Conc Norm by Dose", "Cmax/D", "Plasma/Blood/Serum", "3",
      "AUCIFO", "AUC Infinity Obs", "AUCinf obs", "Plasma/Blood/Serum", "4",
      "AUCIFP", "AUC Infinity Pred", "AUCinf pred", "Plasma/Blood/Serum", "5",
      "AUCIFOD", "AUC Infinity Obs Norm by Dose", "AUCinf/D obs", "Plasma/Blood/Serum", "6",
      "AUCIFD", "AUC Infinity Pred Norm by Dose", "AUCinf/D pred", "Plasma/Blood/Serum", "7",
      "AUCPEO", "AUC %Extrapolation Obs", "AUCinf extrap obs", "Plasma/Blood/Serum", "8",
      "AUCPEP", "AUC %Extrapolation Pred", "AUCinf extrap pred", "Plasma/Blood/Serum", "9",
      "AUCINT", "AUC from T1 to T2", "AUCupper-lower ", "Plasma/Blood/Serum", "10",
      "AUCTAU", "AUC Over Dosing Interval", "AUCtau", "Plasma/Blood/Serum", "11",
      "AUCLST", "AUC to Last Nonzero Conc", "AUClast", "Plasma/Blood/Serum", "12",
      "AUCALL", "AUC All", "AUCall", "Plasma/Blood/Serum", "13",
      "AUMCIFO", "AUMC Infinity Obs", "AUMCinf obs", "Plasma/Blood/Serum", "14",
      "AUMCIFP", "AUMC Infinity Pred", "AUMCinf pred", "Plasma/Blood/Serum", "15",
      "AUMCPEO", "AUMC % Extrapolation Obs", "AUMC extrap obs", "Plasma/Blood/Serum", "16",
      "AUMCPEP", "AUMC % Extrapolation Pred", "AUMC extrap pred", "Plasma/Blood/Serum", "17",
      "AUMCTAU", "AUMC Over Dosing Interval", "AUMCtau", "Plasma/Blood/Serum", "18",
      "AUMCLST", "AUMC to Last Nonzero Conc", "AUMClast", "Plasma/Blood/Serum", "19",
      "AURCIFO", "AURC Infinity Obs", "AURCinf obs", "Plasma/Blood/Serum", "20",
      "AURCIFP", "AURC Infinity Pred", "AURCinf pred", "Plasma/Blood/Serum", "21",
      "AURCPEO", "AURC % Extrapolation Obs", "AURC extrap obs", "Plasma/Blood/Serum", "22",
      "AURCPEP", "AURC % Extrapolation Pred", "AURC extrap pred", "Plasma/Blood/Serum", "23",
      "AURCLST", "AURC Dosing to Last Conc", "AURClast", "Plasma/Blood/Serum", "24",
      "AURCALL", "AURC All", "AURCall", "Plasma/Blood/Serum", "25",
      "TLST", "Time of Last Nonzero Conc", "Tlast", "Plasma/Blood/Serum", "26",
      "CO", "Initial Conc", "CO", "Plasma/Blood/Serum", "27",
      "C0", "Initial Conc", "C0", "Plasma/Blood/Serum", "28",
      "CAVG", "Average Conc", "Cavg", "Plasma/Blood/Serum", "29",
      "CLST", "Last Nonzero Conc", "Clast", "Plasma/Blood/Serum", "30",
      "CMIN", "Min Conc", "Cmin", "Plasma/Blood/Serum", "31",
      "LAMZHL", "Half-Life Lambda z", "t1/2", "Plasma/Blood/Serum", "32",
      "CLFO", "Total CL Obs by F", "CL/F obs", "Plasma/Blood/Serum", "33",
      "CLFP", "Total CL Pred by F", "CL/F pred", "Plasma/Blood/Serum", "34",
      "CLO", "Total CL Obs", "CL obs", "Plasma/Blood/Serum", "35",
      "CLP", "Total CL Pred", "CL pred", "Plasma/Blood/Serum", "36",
      "CLSS", "Total CL Steady State Pred", "CLss", "Plasma/Blood/Serum", "37",
      "CLSSF", "Total CL Steady State Pred by F", "CLss/F", "Plasma/Blood/Serum", "38",
      "VZFO", "Vz Obs by F", "Vz/F obs", "Plasma/Blood/Serum", "39",
      "VZFP", "Vz Pred by F", "Vz/F pred", "Plasma/Blood/Serum", "40",
      "VZO", "Vz Obs", "Vz obs", "Plasma/Blood/Serum", "41",
      "VZP", "Vz Pred", "Vz pred", "Plasma/Blood/Serum", "42",
      "VSSO", "Vol Dist Steady State Obs", "Vss obs", "Plasma/Blood/Serum", "43",
      "VSSP", "Vol Dist Steady State Pred", "Vss pred", "Plasma/Blood/Serum", "44",
      "LAMZ", "Lambda z", "Lambda z", "Plasma/Blood/Serum", "45",
      "LAMZLL", "Lambda z Lower Limit", "Lambda z lower", "Plasma/Blood/Serum", "46",
      "LAMZUL", "Lambda z Upper Limit", "Lambda z upper", "Plasma/Blood/Serum", "47",
      "LAMZNPT", "Number of Points for Lambda z", "No points Lambda z", "Plasma/Blood/Serum", "48",
      "MRTIFO", "MRT Infinity Obs", "MRTinf obs", "Plasma/Blood/Serum", "49",
      "MRTIFP", "MRT Infinity Pred", "MRTinf pred", "Plasma/Blood/Serum", "50",
      "MRTLST", "MRT to Last Nonzero Conc", "MRTlast", "Plasma/Blood/Serum", "51",
      "R2", "R Squared", "Rsq", "Plasma/Blood/Serum", "52",
      "R2ADJ", "R Squared Adjusted", "Rsq adjusted", "Plasma/Blood/Serum", "53",
      "TLAG", "Time Until First Nonzero Conc", "TIag", "Plasma/Blood/Serum", "54",
      "TMIN", "Time of CMIN Observation", "Tmin", "Plasma/Blood/Serum", "55",
      "ACCI", "Accumulation Index", "Accumulation Index", "Plasma/Blood/Serum/Urine", "56",
      "FLUCP", "Fluctuation%", "Fluctuation", "Plasma/Blood/Serum", "57",
      "CORRXY", "Correlation Between TimeX and Log ConcY", "Corr xy", "Plasma/Blood/Serum", "58",
      "RCAMINT", "Amt Rec from T1 to T2", "Ae", "Urine", "59",
      "RCPCINT", "Pct Rec from T1 to T2", "Fe", "Urine", "60",
      "VOLPK", "Sum of Urine Vol", "Urine volume", "Urine", "61",
      "RENALCL", "Renal CL", "CLR", "Plasma/Blood/Serum/Urine", "62",
      "ERTMAX", "Time of Max Excretion Rate", "Tmax Rate", "Urine", "63",
      "RMAX", "Time of Maximum Response", "Rmax", "Matrix of PD", "64",
      "RMIN", "Time of Minimum Response", "Rmin", "Matrix of PD", "65",
      "ERMAX", "Max Excretion Rate", "Max excretion rate", "Urine", "66",
      "MIDPTLST", "Midpoint of Collection Interval", "Midpoint last", "Urine", "67",
      "ERLST", "Last Meas Excretion Rate", "Rate last", "Urine", "68",
      "TON", "Time to Onset", "Tonset", "Matrix of PD", "69",
      "TOFF", "Time to Offset", "Toffset", "Matrix of PD", "70",
      "TBBLP", "Time Below Baseline %", "Time %Below Baseline", "Matrix of PD", "71",
      "TBTP", "Time Below Threshold %", "Time %Below Threshold", "Matrix of PD", "72",
      "TABL", "Time Above Baseline", "Time Above Baseline", "Matrix of PD", "73",
      "TAT", "Time Above Threshold", "Time Above Threshold", "Matrix of PD", "74",
      "TBT", "Time Below Threshold", "Time Below Threshold", "Matrix of PD", "75",
      "TBLT", "Time Between Baseline and Threshold", "Time Between Baseline Threshold", "Matrix of PD", "76",
      "BLRSP", "Baseline Response", "Baseline", "Matrix of PD", "77",
      "TSHDRSP", "Response Threshold", "Threshold", "Matrix of PD", "78",
      "AUCABL", "AUC Above Baseline", "AUC above baseline", "Matrix of PD", "79",
      "AUCAT", "AUC Above Threshold", "AUC above threshold", "Matrix of PD", "80",
      "AUCBBL", "AUC Below Baseline", "AUC below baseline", "Matrix of PD", "81",
      "AUCBT", "AUC Below Threshold", "AUC below threshold", "Matrix of PD", "82",
      "AUCBLDIF", "Diff AUC Above Base and AUC Below Base", "AUC diff baseline", "Matrix of PD", "83",
      "AUCTDIF", "Diff AUC Above Thr and AUC Below Thr", "AUCnet threshold", "Matrix of PD", "84",
      "TDIFF", "Diff Time to Offset and Time to Onset", "Diff toffset-tonset", "Matrix of PD", "85",
      "AUCPBEO", "AUC %Back Extrapolation Obs", "AUC%Back extrap obs", "Plasma/Blood/Serum", "86",
      "AUCPBEP", "AUC %Back Extrapolation Pred", "AUC%Back extrap pred", "Plasma/Blood/Serum", "87",
      "TSLP1L", "Lower Time Limit Slope 1st", "Slope1 lower", "Matrix of PD", "88",
      "TSLP1U", "Upper Time Limit Slope 1st Segment", "Slope1 upper", "Matrix of PD", "89",
      "TSLP2L", "Lower Time Limit Slope 2nd Segment", "Slope2 lower", "Matrix of PD", "90",
      "TSLP2U", "Upper Time Limit Slope 2nd Segment", "Slope2 upper", "Matrix of PD", "91",
      "SLP1", "Slope, 1st Segment", "Slope1", "Matrix of PD", "92",
      "SLP2", "Slope, 2nd Segment", "Slope2", "Matrix of PD", "93",
      "SLP1PT", "Number of Points for Slope 1st Segment", "No points slope1", "Matrix of PD", "94",
      "SLP2PT", "Number of Points for Slope 2nd Segment", "No points slope2", "Matrix of PD", "95",
      "R2ADJS1", "R-Squared Adjusted Slope, 1st Segment", "Rsq adjusted slope1", "Matrix of PD", "96",
      "R2ADJS2", "R-Squared Adjusted Slope, 2nd Segment", "Rsq adjusted slope2", "Matrix of PD", "97",
      "R2SLP1", "R Squared, Slope, 1st Segment", "Rsq slope1", "Matrix of PD", "98",
      "R2SLP2", "R Squared, Slope, 2nd Segment", "Rsq slope2", "Matrix of PD", "99",
      "CORRXYS1", "Corr Btw TimeX and Log ConcY, Slope 1st", "Corr xy slope1", "Plasma/Blood/Serum", "100",
      "CORRXYS2", "Corr Btw TimeX and Log ConcY, Slope 1st Slope 2nd", "Corr xy slope2", "Plasma/Blood/Serum", "101",
      "AILAMZ", "Accumulation Index using Lambda z", "AILAMZ", "Plasma/Blood/Serum", "102",
      "ARAUC", "Accumulation Ratio AUCTAU", "ARAUC", "Plasma/Blood/Serum", "103",
      "ARAUCD", "Accum Ratio AUCTAU norm by dose", "ARAUCD", "Plasma/Blood/Serum", "104",
      "ARAUCIFO", "Accum Ratio AUC Infinity Obs", "ARAUCIFO", "Plasma/Blood/Serum", "105",
      "ARAUCIFP", "Accum Ratio AUC Infinity Pred", "ARAUCIFP", "Plasma/Blood/Serum", "106",
      "ARAUCIND", "Accum Ratio AUC T1 to T2 norm by dose", "ARAUCIND_T1_T2_UNIT", "Plasma/Blood/Serum", "107",
      "ARAUCINT", "Accumulation Ratio AUC from T1 to T2", "ARAUCINT_T1_T2_UNIT", "Plasma/Blood/Serum", "108",
      "ARAUCIOD", "Accum Ratio AUCIFO Norm by Dose", "ARAUCIOD", "Plasma/Blood/Serum", "109",
      "ARAUCIPD", "Accum Ratio AUCIFP Norm by Dose", "ARAUCIPD", "Plasma/Blood/Serum", "110",
      "ARAUCLST", "Accum Ratio AUC to Last Nonzero Conc", "ARAUCLST", "Plasma/Blood/Serum", "111",
      "ARCMAX", "Accumulation Ratio Cmax", "ARCMAX", "Plasma/Blood/Serum", "112",
      "ARCMAXD", "Accum Ratio Cmax norm by dose", "ARCMAXD", "Plasma/Blood/Serum", "113",
      "ARCMIN", "Accumulation Ratio Cmin", "ARCMIN", "Plasma/Blood/Serum", "114",
      "ARCMIND", "Accum Ratio Cmin norm by dose", "ARCMIND", "Plasma/Blood/Serum", "115",
      "ARCTROUD", "Accum Ratio Ctrough norm by dose", "ARCTROUD", "Plasma/Blood/Serum", "116",
      "ARCTROUG", "Accumulation Ratio Ctrough", "ARCTROUG", "Plasma/Blood/Serum", "117",
      "AUCALLB", "AUC All Norm by BMI", "AUCall_B", "Plasma/Blood/Serum", "118",
      "AUCALLD", "AUC All Norm by Dose", "AUCall_D", "Plasma/Blood/Serum", "119",
      "AUCALLS", "AUC All Norm by SA", "AUCall_S", "Plasma/Blood/Serum", "120",
      "AUCALLW", "AUC All Norm by WT", "AUCall_W", "Plasma/Blood/Serum", "121",
      "AUCIFOB", "AUC Infinity Obs Norm by BMI", "AUCINF_obs_B", "Plasma/Blood/Serum", "122",
      "AUCIFOLN", "AUC Infinity Obs LN Transformed", "AUCIFOLN", "Plasma/Blood/Serum", "123",
      "AUCIFOS", "AUC Infinity Obs Norm by SA", "AUCINF_obs_S", "Plasma/Blood/Serum", "124",
      "AUCIFOUB", "AUC Infinity Obs, Unbound Drug", "AUCIFOUB", "Plasma/Blood/Serum", "125",
      "AUCIFOW", "AUC Infinity Obs Norm by WT", "AUCINF_obs_W", "Plasma/Blood/Serum", "126",
      "AUCIFPB", "AUC Infinity Pred Norm by BMI", "AUCINF_pred_B", "Plasma/Blood/Serum", "127",
      "AUCIFPD", "AUC Infinity Pred Norm by Dose", "AUCINF_pred_D", "Plasma/Blood/Serum", "128",
      "AUCIFPS", "AUC Infinity Pred Norm by SA", "AUCINF_pred_S", "Plasma/Blood/Serum", "129",
      "AUCIFPUB", "AUC Infinity Pred, Unbound Drug", "AUCIFPUB", "Plasma/Blood/Serum", "130",
      "AUCIFPW", "AUC Infinity Pred Norm by WT", "AUCINF_pred_W", "Plasma/Blood/Serum", "131",
      "AUCINTB", "AUC from T1 to T2 Norm by BMI", "AUC_B_T1_T2_UNIT", "Plasma/Blood/Serum", "132",
      "AUCINTD", "AUC from T1 to T2 Norm by Dose", "AUC_D_T1_T2_UNIT", "Plasma/Blood/Serum", "133",
      "AUCINTS", "AUC from T1 to T2 Norm by SA", "AUC_S_T1_T2_UNIT", "Plasma/Blood/Serum", "134",
      "AUCINTW", "AUC from T1 to T2 Norm by WT", "AUC_W_T1_T2_UNIT", "Plasma/Blood/Serum", "135",
      "AUCLSTB", "AUC to Last Nonzero Conc Norm by BMI", "AUClast_B", "Plasma/Blood/Serum", "136",
      "AUCLSTD", "AUC to Last Nonzero Conc Norm by Dose", "AUClast_D", "Plasma/Blood/Serum", "137",
      "AUCLSTLN", "AUC to Last Nonzero Conc LN Transformed", "AUCLSTLN", "Plasma/Blood/Serum", "138",
      "AUCLSTS", "AUC to Last Nonzero Conc Norm by SA", "AUClast_S", "Plasma/Blood/Serum", "139",
      "AUCLSTUB", "AUC to Last Nonzero Conc, Unbound Drug", "AUCLSTUB", "Plasma/Blood/Serum", "140",
      "AUCLSTW", "AUC to Last Nonzero Conc Norm by WT", "AUClast_W", "Plasma/Blood/Serum", "141",
      "AUCTAUB", "AUC Over Dosing Interval Norm by BMI", "AUC_TAU_B", "Plasma/Blood/Serum", "142",
      "AUCTAUD", "AUC Over Dosing Interval Norm by Dose", "AUC_TAU_D", "Plasma/Blood/Serum", "143",
      "AUCTAUS", "AUC Over Dosing Interval Norm by SA", "AUC_TAU_S", "Plasma/Blood/Serum", "144",
      "AUCTAUW", "AUC Over Dosing Interval Norm by WT", "AUC_TAU_W", "Plasma/Blood/Serum", "145",
      "AUMCIFOB", "AUMC Infinity Obs Norm by BMI", "AUMCINF_obs_B", "Plasma/Blood/Serum", "146",
      "AUMCIFOD", "AUMC Infinity Obs Norm by Dose", "AUMCINF_obs_D", "Plasma/Blood/Serum", "147",
      "AUMCIFOS", "AUMC Infinity Obs Norm by SA", "AUMCINF_obs_S", "Plasma/Blood/Serum", "148",
      "AUMCIFOW", "AUMC Infinity Obs Norm by WT", "AUMCINF_obs_W", "Plasma/Blood/Serum", "149",
      "AUMCIFPB", "AUMC Infinity Pred Norm by BMI", "AUMCINF_pred_B", "Plasma/Blood/Serum", "150",
      "AUMCIFPD", "AUMC Infinity Pred Norm by Dose", "AUMCINF_pred_D", "Plasma/Blood/Serum", "151",
      "AUMCIFPS", "AUMC Infinity Pred Norm by SA", "AUMCINF_pred_S", "Plasma/Blood/Serum", "152",
      "AUMCIFPW", "AUMC Infinity Pred Norm by WT", "AUMCINF_pred_W", "Plasma/Blood/Serum", "153",
      "AUMCLSTB", "AUMC to Last Nonzero Conc Norm by BMI", "AUMClast_B", "Plasma/Blood/Serum", "154",
      "AUMCLSTD", "AUMC to Last Nonzero Conc Norm by Dose", "AUMClast_D", "Plasma/Blood/Serum", "155",
      "AUMCLSTS", "AUMC to Last Nonzero Conc Norm by SA", "AUMClast_S", "Plasma/Blood/Serum", "156",
      "AUMCLSTW", "AUMC to Last Nonzero Conc Norm by WT", "AUMClast_W", "Plasma/Blood/Serum", "157",
      "AUMCTAUB", "AUMC Over Dosing Interval Norm by BMI", "AUMCTAUB", "Plasma/Blood/Serum", "158",
      "AUMCTAUD", "AUMC Over Dosing Interval Norm by Dose", "AUMCTAUD", "Plasma/Blood/Serum", "159",
      "AUMCTAUS", "AUMC Over Dosing Interval Norm by SA", "AUMCTAUS", "Plasma/Blood/Serum", "160",
      "AUMCTAUW", "AUMC Over Dosing Interval Norm by WT", "AUMCTAUW", "Plasma/Blood/Serum", "161",
      "AURCALLB", "AURC All Norm by BMI", "AURCALLB", "Plasma/Blood/Serum", "162",
      "AURCALLD", "AURC All Norm by Dose", "AURCALLD", "Plasma/Blood/Serum", "163",
      "AURCALLS", "AURC All Norm by SA", "AURCALLS", "Plasma/Blood/Serum", "164",
      "AURCALLW", "AURC All Norm by WT", "AURCALLW", "Plasma/Blood/Serum", "165",
      "AURCIFOB", "AURC Infinity Obs Norm by BMI", "AURCIFOB", "Plasma/Blood/Serum", "166",
      "AURCIFOD", "AURC Infinity Obs Norm by Dose", "AURCIFOD", "Plasma/Blood/Serum", "167",
      "AURCIFOS", "AURC Infinity Obs Norm by SA", "AURCIFOS", "Plasma/Blood/Serum", "168",
      "AURCIFOW", "AURC Infinity Obs Norm by WT", "AURCIFOW", "Plasma/Blood/Serum", "169",
      "AURCIFPB", "AURC Infinity Pred Norm by BMI", "AURCIFPB", "Plasma/Blood/Serum", "170",
      "AURCIFPD", "AURC Infinity Pred Norm by Dose", "AURCIFPD", "Plasma/Blood/Serum", "171",
      "AURCIFPS", "AURC Infinity Pred Norm by SA", "AURCIFPS", "Plasma/Blood/Serum", "172",
      "AURCIFPW", "AURC Infinity Pred Norm by WT", "AURCIFPW", "Plasma/Blood/Serum", "173",
      "AURCINT", "AURC from T1 to T2", "AURCINT_T1_T2_UNIT", "Plasma/Blood/Serum", "174",
      "AURCINTB", "AURC from T1 to T2 Norm by BMI", "AURCINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "175",
      "AURCINTD", "AURC from T1 to T2 Norm by Dose", "AURCINTD_T1_T2_UNIT", "Plasma/Blood/Serum", "176",
      "AURCINTS", "AURC from T1 to T2 Norm by SA", "AURCINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "177",
      "AURCINTW", "AURC from T1 to T2 Norm by WT", "AURCINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "178",
      "AURCLSTB", "AURC to Last Nonzero Rate Norm by BMI", "AURCLSTB", "Plasma/Blood/Serum", "179",
      "AURCLSTD", "AURC to Last Nonzero Rate Norm by Dose", "AURCLSTD", "Plasma/Blood/Serum", "180",
      "AURCLSTS", "AURC to Last Nonzero Rate Norm by SA", "AURCLSTS", "Plasma/Blood/Serum", "181",
      "AURCLSTW", "AURC to Last Nonzero Rate Norm by WT", "AURCLSTW", "Plasma/Blood/Serum", "182",
      "C0B", "Initial Conc Norm by BMI", "C0B", "Plasma/Blood/Serum", "183",
      "C0D", "Initial Conc Norm by Dose", "C0D", "Plasma/Blood/Serum", "184",
      "C0S", "Initial Conc Norm by SA", "C0S", "Plasma/Blood/Serum", "185",
      "C0W", "Initial Conc Norm by WT", "C0W", "Plasma/Blood/Serum", "186",
      "CAVGB", "Average Conc Norm by BMI", "CAVGB", "Plasma/Blood/Serum", "187",
      "CAVGD", "Average Conc Norm by Dose", "CAVGD", "Plasma/Blood/Serum", "188",
      "CAVGINT", "Average Conc from T1 to T2", "CAVGINT_T1_T2_UNIT", "Plasma/Blood/Serum", "189",
      "CAVGINTB", "Average Conc from T1 to T2 Norm by BMI", "CAVGINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "190",
      "CAVGINTD", "Average Conc from T1 to T2 Norm by Dose", "CAVGINTD_T1_T2_UNIT", "Plasma/Blood/Serum", "191",
      "CAVGINTS", "Average Conc from T1 to T2 Norm by SA", "CAVGINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "192",
      "CAVGINTW", "Average Conc from T1 to T2 Norm by WT", "CAVGINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "193",
      "CAVGS", "Average Conc Norm by SA", "CAVGS", "Plasma/Blood/Serum", "194",
      "CAVGW", "Average Conc Norm by WT", "CAVGW", "Plasma/Blood/Serum", "195",
      "CHTMAX", "Concentration at Half Tmax", "CHTMAX", "Plasma/Blood/Serum", "196",
      "CLFOB", "Total CL Obs by F Norm by BMI", "CLFOB", "Plasma/Blood/Serum", "197",
      "CLFOD", "Total CL Obs by F Norm by Dose", "CLFOD", "Plasma/Blood/Serum", "198",
      "CLFOS", "Total CL Obs by F Norm by SA", "CLFOS", "Plasma/Blood/Serum", "199",
      "CLFOW", "Total CL Obs by F Norm by WT", "CLFOW", "Plasma/Blood/Serum", "200",
      "CLFPB", "Total CL Pred by F Norm by BMI", "CLFPB", "Plasma/Blood/Serum", "201",
      "CLFPD", "Total CL Pred by F Norm by Dose", "CLFPD", "Plasma/Blood/Serum", "202",
      "CLFPS", "Total CL Pred by F Norm by SA", "CLFPS", "Plasma/Blood/Serum", "203",
      "CLFPW", "Total CL Pred by F Norm by WT", "CLFPW", "Plasma/Blood/Serum", "204",
      "CLFTAU", "Total CL by F for Dose Int", "CLFTAU", "Plasma/Blood/Serum", "205",
      "CLFTAUB", "Total CL by F for Dose Int Norm by BMI", "CLFTAUB", "Plasma/Blood/Serum", "206",
      "CLFTAUD", "Total CL by F for Dose Int Norm by Dose", "CLFTAUD", "Plasma/Blood/Serum", "207",
      "CLFTAUS", "Total CL by F for Dose Int Norm by SA", "CLFTAUS", "Plasma/Blood/Serum", "208",
      "CLFTAUW", "Total CL by F for Dose Int Norm by WT", "CLFTAUW", "Plasma/Blood/Serum", "209",
      "CLFUB", "Apparent CL for Unbound Drug", "CLFUB", "Plasma/Blood/Serum", "210",
      "CLOB", "Total CL Obs Norm by BMI", "CLOB", "Plasma/Blood/Serum", "211",
      "CLOD", "Total CL Obs Norm by Dose", "CLOD", "Plasma/Blood/Serum", "212",
      "CLOS", "Total CL Obs Norm by SA", "CLOS", "Plasma/Blood/Serum", "213",
      "CLOUB", "Total CL Obs for Unbound Drug", "CLOUB", "Plasma/Blood/Serum", "214",
      "CLOW", "Total CL Obs Norm by WT", "CLOW", "Plasma/Blood/Serum", "215",
      "CLPB", "Total CL Pred Norm by BMI", "CLPB", "Plasma/Blood/Serum", "216",
      "CLPD", "Total CL Pred Norm by Dose", "CLPD", "Plasma/Blood/Serum", "217",
      "CLPS", "Total CL Pred Norm by SA", "CLPS", "Plasma/Blood/Serum", "218",
      "CLPUB", "Total CL Pred for Unbound Drug", "CLPUB", "Plasma/Blood/Serum", "219",
      "CLPW", "Total CL Pred Norm by WT", "CLPW", "Plasma/Blood/Serum", "220",
      "CLRPCLEV", "Renal CL as Pct CL EV", "CLRPCLEV", "Urine", "221",
      "CLRPCLIV", "Renal CL as Pct CL IV", "CLRPCLIV", "Urine", "222",
      "CLSTB", "Last Nonzero Conc Norm by BMI", "CLSTB", "Plasma/Blood/Serum", "223",
      "CLSTD", "Last Nonzero Conc Norm by Dose", "CLSTD", "Plasma/Blood/Serum", "224",
      "CLSTS", "Last Nonzero Conc Norm by SA", "CLSTS", "Plasma/Blood/Serum", "225",
      "CLSTW", "Last Nonzero Conc Norm by WT", "CLSTW", "Plasma/Blood/Serum", "226",
      "CLTAU", "Total CL for Dose Int", "CLTAU", "Plasma/Blood/Serum", "227",
      "CLTAUB", "Total CL for Dose Int Norm by BMI", "CLTAUB", "Plasma/Blood/Serum", "228",
      "CLTAUD", "Total CL for Dose Int Norm by Dose", "CLTAUD", "Plasma/Blood/Serum", "229",
      "CLTAUS", "Total CL for Dose Int Norm by SA", "CLTAUS", "Plasma/Blood/Serum", "230",
      "CLTAUW", "Total CL for Dose Int Norm by WT", "CLTAUW", "Plasma/Blood/Serum", "231",
      "CMAXB", "Max Conc Norm by BMI", "CMAX_B", "Plasma/Blood/Serum", "232",
      "CMAXLN", "Max Conc LN Transformed", "CMAXLN", "Plasma/Blood/Serum", "233",
      "CMAXS", "Max Conc Norm by SA", "CMAXS", "Plasma/Blood/Serum", "234",
      "CMAXUB", "Max Conc, Unbound Drug", "CMAXUB", "Plasma/Blood/Serum", "235",
      "CMAXW", "Max Conc Norm by WT", "CMAXW", "Plasma/Blood/Serum", "236",
      "CMINB", "Min Conc Norm by BMI", "CMINB", "Plasma/Blood/Serum", "237",
      "CMIND", "Min Conc Norm by Dose", "CMIND", "Plasma/Blood/Serum", "238",
      "CMINS", "Min Conc Norm by SA", "CMINS", "Plasma/Blood/Serum", "239",
      "CMINW", "Min Conc Norm by WT", "CMINW", "Plasma/Blood/Serum", "240",
      "CONC", "Concentration", "CONC", "Plasma/Blood/Serum", "241",
      "CONCB", "Conc by BMI", "CONCB", "Plasma/Blood/Serum", "242",
      "CONCD", "Conc by Dose", "CONCD", "Plasma/Blood/Serum", "243",
      "CONCS", "Conc by SA", "CONCS", "Plasma/Blood/Serum", "244",
      "CONCW", "Conc by WT", "CONCW", "Plasma/Blood/Serum", "245",
      "CTROUGH", "Conc Trough", "CTROUGH", "Plasma/Blood/Serum", "246",
      "CTROUGHB", "Conc Trough by BMI", "CTROUGHB", "Plasma/Blood/Serum", "247",
      "CTROUGHD", "Conc Trough by Dose", "CTROUGHD", "Plasma/Blood/Serum", "248",
      "CTROUGHS", "Conc Trough by SA", "CTROUGHS", "Plasma/Blood/Serum", "249",
      "CTROUGHW", "Conc Trough by WT", "CTROUGHW", "Plasma/Blood/Serum", "250",
      "EFFHL", "Effective Half-Life", "EFFHL", "Plasma/Blood/Serum", "251",
      "ERINT", "Excret Rate from T1 to T2", "ERINT_T1_T2_UNIT", "Plasma/Blood/Serum", "252",
      "ERINTB", "Excret Rate from T1 to T2 Norm by BMI", "ERINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "253",
      "ERINTD", "Excret Rate from T1 to T2 Norm by Dose", "ERINTD_T1_T2_UNIT", "Plasma/Blood/Serum", "254",
      "ERINTS", "Excret Rate from T1 to T2 Norm by SA", "ERINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "255",
      "ERINTW", "Excret Rate from T1 to T2 Norm by WT", "ERINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "256",
      "ERLSTB", "Last Meas Excretion Rate Norm by BMI", "ERLSTB", "Plasma/Blood/Serum", "257",
      "ERLSTD", "Last Meas Excretion Rate Norm by Dose", "ERLSTD", "Plasma/Blood/Serum", "258",
      "ERLSTS", "Last Meas Excretion Rate Norm by SA", "ERLSTS", "Plasma/Blood/Serum", "259",
      "ERLSTW", "Last Meas Excretion Rate Norm by WT", "ERLSTW", "Plasma/Blood/Serum", "260",
      "ERMAXB", "Max Excretion Rate Norm by BMI", "ERMAXB", "Plasma/Blood/Serum", "261",
      "ERMAXD", "Max Excretion Rate Norm by Dose", "ERMAXD", "Plasma/Blood/Serum", "262",
      "ERMAXS", "Max Excretion Rate Norm by SA", "ERMAXS", "Plasma/Blood/Serum", "263",
      "ERMAXW", "Max Excretion Rate Norm by WT", "ERMAXW", "Plasma/Blood/Serum", "264",
      "ERTLST", "Midpoint of Interval of Last Nonzero ER", "ERTLST", "Plasma/Blood/Serum", "265",
      "FABS", "Absolute Bioavailability", "FABS", "Plasma/Blood/Serum", "266",
      "FB", "Fraction Bound", "FB", "Plasma/Blood/Serum", "267",
      "FREL", "Relative Bioavailability", "FREL", "Plasma/Blood/Serum", "268",
      "FREXINT", "Fract Excr from T1 to T2", "FREXINT_T1_T2_UNIT", "Plasma/Blood/Serum", "269",
      "FU", "Fraction Unbound", "FU", "Plasma/Blood/Serum", "270",
      "HDCL", "Hemodialysis Clearance", "HDCL", "Plasma/Blood/Serum", "271",
      "HDER", "Hemodialysis Extraction Ratio", "HDER", "Plasma/Blood/Serum", "272",
      "HTMAX", "Half Tmax", "HTMAX", "Plasma/Blood/Serum", "273",
      "LAMZLTAU", "Lambda z Lower Limit TAU", "LAMZLTAU", "Plasma/Blood/Serum", "274",
      "LAMZNTAU", "Number of Points for Lambda z TAU", "LAMZNTAU", "Plasma/Blood/Serum", "275",
      "LAMZSPN", "Lambda z Span", "LAMZSPN", "Plasma/Blood/Serum", "276",
      "LAMZTAU", "Lambda z TAU", "LAMZTAU", "Plasma/Blood/Serum", "277",
      "LAMZUTAU", "Lambda z Upper Limit TAU", "LAMZUTAU", "Plasma/Blood/Serum", "278",
      "MAT", "Mean Absorption Time", "MAT", "Plasma/Blood/Serum", "279",
      "MRAUCIFO", "Metabolite Ratio for AUC Infinity Obs", "MRAUCIFO", "Plasma/Blood/Serum", "280",
      "MRAUCIFP", "Metabolite Ratio for AUC Infinity Pred", "MRAUCIFP", "Plasma/Blood/Serum", "281",
      "MRAUCINT", "Metabolite Ratio AUC from T1 to T2", "MRAUCINT_T1_T2_UNIT", "Plasma/Blood/Serum", "282",
      "MRAUCLST", "Metabolite Ratio AUC Last Nonzero Conc", "MRAUCLST", "Plasma/Blood/Serum", "283",
      "MRAUCTAU", "Metabolite Ratio for AUC Dosing Interval", "MRAUCTAU", "Plasma/Blood/Serum", "284",
      "MRCMAX", "Metabolite Ratio for Max Conc", "MRCMAX", "Plasma/Blood/Serum", "285",
      "MRTEVIFO", "MRT Extravasc Infinity Obs", "MRTEVIFO", "Plasma/Blood/Serum", "286",
      "MRTEVIFP", "MRT Extravasc Infinity Pred", "MRTEVIFP", "Plasma/Blood/Serum", "287",
      "MRTEVLST", "MRT Extravasc to Last Nonzero Conc", "MRTEVLST", "Plasma/Blood/Serum", "288",
      "MRTIVIFO", "MRT Intravasc Infinity Obs", "MRTIVIFO", "Plasma/Blood/Serum", "289",
      "MRTIVIFP", "MRT Intravasc Infinity Pred", "MRTIVIFP", "Plasma/Blood/Serum", "290",
      "MRTIVLST", "MRT Intravasc to Last Nonzero Conc", "MRTIVLST", "Plasma/Blood/Serum", "291",
      "NRENALCL", "Nonrenal CL", "NRENALCL", "Urine", "292",
      "NRENLCLB", "Nonrenal CL Norm by BMI", "NRENLCLB", "Urine", "293",
      "NRENLCLD", "Nonrenal CL Norm by Dose", "NRENLCLD", "Urine", "294",
      "NRENLCLS", "Nonrenal CL Norm by SA", "NRENLCLS", "Urine", "295",
      "NRENLCLW", "Nonrenal CL Norm by WT", "NRENLCLW", "Urine", "296",
      "PTROUGHR", "Peak Trough Ratio", "PTROUGHR", "Plasma/Blood/Serum", "297",
      "RAAUC", "Ratio AUC", "RAAUC", "Plasma/Blood/Serum", "298",
      "RAAUCIFO", "Ratio AUC Infinity Obs", "RAAUCIFO", "Plasma/Blood/Serum", "299",
      "RAAUCIFP", "Ratio AUC Infinity Pred", "RAAUCIFP", "Plasma/Blood/Serum", "300",
      "RACMAX", "Ratio CMAX", "RACMAX", "Plasma/Blood/Serum", "301",
      "RAMAXMIN", "Ratio of CMAX to CMIN", "RAMAXMIN", "Plasma/Blood/Serum", "302",
      "RCAMIFO", "Amt Rec Infinity Obs", "RCAMIFO", "Plasma/Blood/Serum", "303",
      "RCAMIFOB", "Amt Rec Infinity Obs Norm by BMI", "RCAMIFOB", "Plasma/Blood/Serum", "304",
      "RCAMIFOS", "Amt Rec Infinity Obs Norm by SA", "RCAMIFOS", "Plasma/Blood/Serum", "305",
      "RCAMIFOW", "Amt Rec Infinity Obs Norm by WT", "RCAMIFOW", "Plasma/Blood/Serum", "306",
      "RCAMIFP", "Amt Rec Infinity Pred", "RCAMIFP", "Plasma/Blood/Serum", "307",
      "RCAMIFPB", "Amt Rec Infinity Pred Norm by BMI", "RCAMIFPB", "Plasma/Blood/Serum", "308",
      "RCAMIFPS", "Amt Rec Infinity Pred Norm by SA", "RCAMIFPS", "Plasma/Blood/Serum", "309",
      "RCAMIFPW", "Amt Rec Infinity Pred Norm by WT", "RCAMIFPW", "Plasma/Blood/Serum", "310",
      "RCAMINTB", "Amt Rec from T1 to T2 Norm by BMI", "RCAMINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "311",
      "RCAMINTS", "Amt Rec from T1 to T2 Norm by SA", "RCAMINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "312",
      "RCAMINTW", "Amt Rec from T1 to T2 Norm by WT", "RCAMINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "313",
      "RCAMTAU", "Amt Rec Over Dosing Interval", "RCAMTAU", "Plasma/Blood/Serum", "314",
      "RCAMTAUB", "Amt Rec Over Dosing Interval Norm by BMI", "RCAMTAUB", "Plasma/Blood/Serum", "315",
      "RCAMTAUS", "Amt Rec Over Dosing Interval Norm by SA", "RCAMTAUS", "Plasma/Blood/Serum", "316",
      "RCAMTAUW", "Amt Rec Over Dosing Interval Norm by WT", "RCAMTAUW", "Plasma/Blood/Serum", "317",
      "RCPCIFO", "Pct Rec Infinity Obs", "RCPCIFO", "Plasma/Blood/Serum", "318",
      "RCPCIFOB", "Pct Rec Infinity Obs Norm by BMI", "RCPCIFOB", "Plasma/Blood/Serum", "319",
      "RCPCIFOS", "Pct Rec Infinity Obs Norm by SA", "RCPCIFOS", "Plasma/Blood/Serum", "320",
      "RCPCIFOW", "Pct Rec Infinity Obs Norm by WT", "RCPCIFOW", "Plasma/Blood/Serum", "321",
      "RCPCIFP", "Pct Rec Infinity Pred", "RCPCIFP", "Plasma/Blood/Serum", "322",
      "RCPCIFPB", "Pct Rec Infinity Pred Norm by BMI", "RCPCIFPB", "Plasma/Blood/Serum", "323",
      "RCPCIFPS", "Pct Rec Infinity Pred Norm by SA", "RCPCIFPS", "Plasma/Blood/Serum", "324",
      "RCPCIFPW", "Pct Rec Infinity Pred Norm by WT", "RCPCIFPW", "Plasma/Blood/Serum", "325",
      "RCPCINTB", "Pct Rec from T1 to T2 Norm by BMI", "RCPCINTB_T1_T2_UNIT", "Plasma/Blood/Serum", "326",
      "RCPCINTS", "Pct Rec from T1 to T2 Norm by SA", "RCPCINTS_T1_T2_UNIT", "Plasma/Blood/Serum", "327",
      "RCPCINTW", "Pct Rec from T1 to T2 Norm by WT", "RCPCINTW_T1_T2_UNIT", "Plasma/Blood/Serum", "328",
      "RCPCLST", "Pct Rec to Last Nonzero Conc", "RCPCLST", "Plasma/Blood/Serum", "329",
      "RCPCTAU", "Pct Rec Over Dosing Interval", "RCPCTAU", "Plasma/Blood/Serum", "330",
      "RCPCTAUB", "Pct Rec Over Dosing Interval Norm by BMI", "RCPCTAUB", "Plasma/Blood/Serum", "331",
      "RCPCTAUS", "Pct Rec Over Dosing Interval Norm by SA", "RCPCTAUS", "Plasma/Blood/Serum", "332",
      "RCPCTAUW", "Pct Rec Over Dosing Interval Norm by WT", "RCPCTAUW", "Plasma/Blood/Serum", "333",
      "RENALCLB", "Renal CL Norm by BMI", "RENALCLB", "Urine", "334",
      "RENALCLD", "Renal CL Norm by Dose", "RENALCLD", "Urine", "335",
      "RENALCLS", "Renal CL Norm by SA", "RENALCLS", "Urine", "336",
      "RENALCLW", "Renal CL Norm by WT", "RENALCLW", "Urine", "337",
      "RENCLTAU", "Renal CL for Dose Int", "RENCLTAU", "Urine", "338",
      "RNCLINT", "Renal CL from T1 to T2", "RNCLINT_T1_T2_UNIT", "Urine", "339",
      "RNCLINTB", "Renal CL from T1 to T2 Norm by BMI", "RNCLINTB_T1_T2_UNIT", "Urine", "340",
      "RNCLINTD", "Renal CL from T1 to T2 Norm by Dose", "RNCLINTD_T1_T2_UNIT", "Urine", "341",
      "RNCLINTS", "Renal CL from T1 to T2 Norm by SA", "RNCLINTS_T1_T2_UNIT", "Urine", "342",
      "RNCLINTW", "Renal CL from T1 to T2 Norm by WT", "RNCLINTW_T1_T2_UNIT", "Urine", "343",
      "RNCLTAUB", "Renal CL for Dose Int Norm by BMI", "RNCLTAUB", "Urine", "344",
      "RNCLTAUD", "Renal CL for Dose Int Norm by Dose", "RNCLTAUD", "Urine", "345",
      "RNCLTAUS", "Renal CL for Dose Int Norm by SA", "RNCLTAUS", "Urine", "346",
      "RNCLTAUW", "Renal CL for Dose Int Norm by WT", "RNCLTAUW", "Urine", "347",
      "RNCLUB", "Renal CL for Unbound Drug", "RNCLUB", "Urine", "348",
      "SRAUC", "Stationarity Ratio AUC", "SRAUC", "Plasma/Blood/Serum", "349",
      "SWING", "Swing", "SWING", "Plasma/Blood/Serum", "350",
      "TAUHL", "Half-Life TAU", "TAUHL", "Plasma/Blood/Serum", "351",
      "TBBL", "Time Below Baseline", "Time_Below_B", "Plasma/Blood/Serum", "352",
      "TROUGHPR", "Trough Peak Ratio", "TROUGHPR", "Plasma/Blood/Serum", "353",
      "V0", "Vol Dist Initial", "V0", "Plasma/Blood/Serum", "354",
      "V0B", "Vol Dist Initial Norm by BMI", "V0B", "Plasma/Blood/Serum", "355",
      "V0D", "Vol Dist Initial Norm by Dose", "V0D", "Plasma/Blood/Serum", "356",
      "V0S", "Vol Dist Initial Norm by SA", "V0S", "Plasma/Blood/Serum", "357",
      "V0W", "Vol Dist Initial Norm by WT", "V0W", "Plasma/Blood/Serum", "358",
      "VSSOB", "Vol Dist Steady State Obs Norm by BMI", "VSSOB", "Plasma/Blood/Serum", "359",
      "VSSOBD", "Vol Dist Steady State Obs by B", "VSSOBD", "Plasma/Blood/Serum", "360",
      "VSSOD", "Vol Dist Steady State Obs Norm by Dose", "VSSOD", "Plasma/Blood/Serum", "361",
      "VSSOF", "Vol Dist Steady State Obs by F", "VSSOF", "Plasma/Blood/Serum", "362",
      "VSSOS", "Vol Dist Steady State Obs Norm by SA", "VSSOS", "Plasma/Blood/Serum", "363",
      "VSSOUB", "Vol Dist Steady State Obs by UB", "VSSOUB", "Plasma/Blood/Serum", "364",
      "VSSOW", "Vol Dist Steady State Obs Norm by WT", "VSSOW", "Plasma/Blood/Serum", "365",
      "VSSPB", "Vol Dist Steady State Pred Norm by BMI", "VSSPB", "Plasma/Blood/Serum", "366",
      "VSSPBD", "Vol Dist Steady State Pred by B", "VSSPBD", "Plasma/Blood/Serum", "367",
      "VSSPD", "Vol Dist Steady State Pred Norm by Dose", "VSSPD", "Plasma/Blood/Serum", "368",
      "VSSPF", "Vol Dist Steady State Pred by F", "VSSPF", "Plasma/Blood/Serum", "369",
      "VSSPS", "Vol Dist Steady State Pred Norm by SA", "VSSPS", "Plasma/Blood/Serum", "370",
      "VSSPUB", "Vol Dist Steady State Pred by UB", "VSSPUB", "Plasma/Blood/Serum", "371",
      "VSSPW", "Vol Dist Steady State Pred Norm by WT", "VSSPW", "Plasma/Blood/Serum", "372",
      "VZ", "Vol Z", "Vz", "Plasma/Blood/Serum", "373",
      "VZF", "Vol Z by F", "Vz_F", "Plasma/Blood/Serum", "374",
      "VZFOB", "Vz Obs by F Norm by BMI", "VZFOB", "Plasma/Blood/Serum", "375",
      "VZFOD", "Vz Obs by F Norm by Dose", "VZFOD", "Plasma/Blood/Serum", "376",
      "VZFOS", "Vz Obs by F Norm by SA", "VZFOS", "Plasma/Blood/Serum", "377",
      "VZFOUB", "Vz Obs by F for UB", "VZFOUB", "Plasma/Blood/Serum", "378",
      "VZFOW", "Vz Obs by F Norm by WT", "VZFOW", "Plasma/Blood/Serum", "379",
      "VZFPB", "Vz Pred by F Norm by BMI", "VZFPB", "Plasma/Blood/Serum", "380",
      "VZFPD", "Vz Pred by F Norm by Dose", "VZFPD", "Plasma/Blood/Serum", "381",
      "VZFPS", "Vz Pred by F Norm by SA", "VZFPS", "Plasma/Blood/Serum", "382",
      "VZFPUB", "Vz Pred by F for UB", "VZFPUB", "Plasma/Blood/Serum", "383",
      "VZFPW", "Vz Pred by F Norm by WT", "VZFPW", "Plasma/Blood/Serum", "384",
      "VZFTAU", "Vz for Dose Int by F", "VZFTAU", "Plasma/Blood/Serum", "385",
      "VZFTAUB", "Vz for Dose Int by F Norm by BMI", "VZFTAUB", "Plasma/Blood/Serum", "386",
      "VZFTAUD", "Vz for Dose Int by F Norm by Dose", "VZFTAUD", "Plasma/Blood/Serum", "387",
      "VZFTAUS", "Vz for Dose Int by F Norm by SA", "VZFTAUS", "Plasma/Blood/Serum", "388",
      "VZFTAUW", "Vz for Dose Int by F Norm by WT", "VZFTAUW", "Plasma/Blood/Serum", "389",
      "VZOB", "Vz Obs Norm by BMI", "VZOB", "Plasma/Blood/Serum", "390",
      "VZOD", "Vz Obs Norm by Dose", "VZOD", "Plasma/Blood/Serum", "391",
      "VZOS", "Vz Obs Norm by SA", "VZOS", "Plasma/Blood/Serum", "392",
      "VZOUB", "Vz Obs for UB", "VZOUB", "Plasma/Blood/Serum", "393",
      "VZOW", "Vz Obs Norm by WT", "VZOW", "Plasma/Blood/Serum", "394",
      "VZPB", "Vz Pred Norm by BMI", "VZPB", "Plasma/Blood/Serum", "395",
      "VZPD", "Vz Pred Norm by Dose", "VZPD", "Plasma/Blood/Serum", "396",
      "VZPS", "Vz Pred Norm by SA", "VZPS", "Plasma/Blood/Serum", "397",
      "VZPUB", "Vz Pred for UB", "VZPUB", "Plasma/Blood/Serum", "398"
    ),
    ncol = 5,
    byrow = TRUE
  ))
  colnames(pk_dataset) <- c("PARAMCD", "PARAM", "TLG_DISPLAY", "MATRIX", "TLG_ORDER")
  pk_dataset
}

#' Control function for logistic regression model fitting
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This is an auxiliary function for controlling arguments for logistic regression models.
#' `conf_level` refers to the confidence level used for the Odds Ratio CIs.
#'
#' @inheritParams argument_convention
#' @param response_definition (`string`)\cr the definition of what an event is in terms of `response`.
#'   This will be used when fitting the logistic regression model on the left hand side of the formula.
#'   Note that the evaluated expression should result in either a logical vector or a factor with 2
#'   levels. By default this is just `"response"` such that the original response variable is used
#'   and not modified further.
#'
#' @return A list of components with the same names as the arguments.
#'
#' @examples
#' # Standard options.
#' control_logistic()
#'
#' # Modify confidence level.
#' control_logistic(conf_level = 0.9)
#'
#' # Use a different response definition.
#' control_logistic(response_definition = "I(response %in% c('CR', 'PR'))")
#'
#' @export
control_logistic <- function(response_definition = "response",
                             conf_level = 0.95) {
  checkmate::assert_true(grepl("response", response_definition))
  checkmate::assert_string(response_definition)
  assert_proportion_value(conf_level)
  list(
    response_definition = response_definition,
    conf_level = conf_level
  )
}

#' Subgroup treatment effect pattern (STEP) fit for binary (response) outcome
#'
#' @description `r lifecycle::badge("stable")`
#'
#' This fits the Subgroup Treatment Effect Pattern logistic regression models for a binary
#' (response) outcome. The treatment arm variable must have exactly 2 levels,
#' where the first one is taken as reference and the estimated odds ratios are
#' for the comparison of the second level vs. the first one.
#'
#' The (conditional) logistic regression model which is fit is:
#'
#' `response ~ arm * poly(biomarker, degree) + covariates + strata(strata)`
#'
#' where `degree` is specified by `control_step()`.
#'
#' @inheritParams argument_convention
#' @param variables (named `list` of `character`)\cr list of analysis variables:
#'   needs `response`, `arm`, `biomarker`, and optional `covariates` and `strata`.
#' @param control (named `list`)\cr combined control list from [control_step()]
#'   and [control_logistic()].
#'
#' @return A matrix of class `step`. The first part of the columns describe the
#'   subgroup intervals used for the biomarker variable, including where the
#'   center of the intervals are and their bounds. The second part of the
#'   columns contain the estimates for the treatment arm comparison.
#'
#' @note For the default degree 0 the `biomarker` variable is not included in the model.
#'
#' @seealso [control_step()] and [control_logistic()] for the available
#'   customization options.
#'
#' @examples
#' # Testing dataset with just two treatment arms.
#' library(survival)
#' library(dplyr)
#'
#' adrs_f <- tern_ex_adrs %>%
#'   filter(
#'     PARAMCD == "BESRSPI",
#'     ARM %in% c("B: Placebo", "A: Drug X")
#'   ) %>%
#'   mutate(
#'     # Reorder levels of ARM to have Placebo as reference arm for Odds Ratio calculations.
#'     ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
#'     RSP = case_when(AVALC %in% c("PR", "CR") ~ 1, TRUE ~ 0),
#'     SEX = factor(SEX)
#'   )
#'
#' variables <- list(
#'   arm = "ARM",
#'   biomarker = "BMRKR1",
#'   covariates = "AGE",
#'   response = "RSP"
#' )
#'
#' # Fit default STEP models: Here a constant treatment effect is estimated in each subgroup.
#' # We use a large enough bandwidth to avoid too small subgroups and linear separation in those.
#' step_matrix <- fit_rsp_step(
#'   variables = variables,
#'   data = adrs_f,
#'   control = c(control_logistic(), control_step(bandwidth = 0.9))
#' )
#' dim(step_matrix)
#' head(step_matrix)
#'
#' # Specify different polynomial degree for the biomarker interaction to use more flexible local
#' # models. Or specify different logistic regression options, including confidence level.
#' step_matrix2 <- fit_rsp_step(
#'   variables = variables,
#'   data = adrs_f,
#'   control = c(control_logistic(conf_level = 0.9), control_step(bandwidth = NULL, degree = 1))
#' )
#'
#' # Use a global constant model. This is helpful as a reference for the subgroup models.
#' step_matrix3 <- fit_rsp_step(
#'   variables = variables,
#'   data = adrs_f,
#'   control = c(control_logistic(), control_step(bandwidth = NULL, num_points = 2L))
#' )
#'
#' # It is also possible to use strata, i.e. use conditional logistic regression models.
#' variables2 <- list(
#'   arm = "ARM",
#'   biomarker = "BMRKR1",
#'   covariates = "AGE",
#'   response = "RSP",
#'   strata = c("STRATA1", "STRATA2")
#' )
#'
#' step_matrix4 <- fit_rsp_step(
#'   variables = variables2,
#'   data = adrs_f,
#'   control = c(control_logistic(), control_step(bandwidth = NULL))
#' )
#'
#' @export
fit_rsp_step <- function(variables,
                         data,
                         control = c(control_step(), control_logistic())) {
  assert_df_with_variables(data, variables)
  checkmate::assert_list(control, names = "named")
  data <- data[!is.na(data[[variables$biomarker]]), ]
  window_sel <- h_step_window(x = data[[variables$biomarker]], control = control)
  interval_center <- window_sel$interval[, "Interval Center"]
  form <- h_step_rsp_formula(variables = variables, control = control)
  estimates <- if (is.null(control$bandwidth)) {
    h_step_rsp_est(
      formula = form,
      data = data,
      variables = variables,
      x = interval_center,
      control = control
    )
  } else {
    tmp <- mapply(
      FUN = h_step_rsp_est,
      x = interval_center,
      subset = as.list(as.data.frame(window_sel$sel)),
      MoreArgs = list(
        formula = form,
        data = data,
        variables = variables,
        control = control
      )
    )
    # Maybe we find a more elegant solution than this.
    rownames(tmp) <- c("n", "logor", "se", "ci_lower", "ci_upper")
    t(tmp)
  }
  result <- cbind(window_sel$interval, estimates)
  structure(
    result,
    class = c("step", "matrix"),
    variables = variables,
    control = control
  )
}

1		#' Summarize change from baseline values or absolute baseline values
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [summarize_change()] creates a layout element to summarize the change from baseline or absolute
6		#' baseline values. The primary analysis variable `vars` indicates the numerical change from baseline results.
7		#'
8		#' Required secondary analysis variables `value` and `baseline_flag` can be supplied to the function via
9		#' the `variables` argument. The `value` element should be the name of the analysis value variable, and the
10		#' `baseline_flag` element should be the name of the flag variable that indicates whether or not records contain
11		#' baseline values. Depending on the baseline flag given, either the absolute baseline values (at baseline)
12		#' or the change from baseline values (post-baseline) are then summarized.
13		#'
14		#' @inheritParams argument_convention
15		#' @param .stats (`character`)\cr statistics to select for the table.
16		#'
17		#' Options are: ``r shQuote(get_stats("analyze_vars_numeric"), type = "sh")``
18		#'
19		#' @name summarize_change
20		#' @order 1
21		NULL
22
23		#' @describeIn summarize_change Statistics function that summarizes baseline or post-baseline visits.
24		#'
25		#' @return
26		#' * `s_change_from_baseline()` returns the same values returned by [s_summary.numeric()].
27		#'
28		#' @note The data in `df` must be either all be from baseline or post-baseline visits. Otherwise
29		#' an error will be thrown.
30		#'
31		#' @keywords internal
32		s_change_from_baseline <- function(df, ...) {
33	10x	args_list <- list(...)
34	10x	.var <- args_list[[".var"]]
35	10x	variables <- args_list[["variables"]]
36
37	10x	checkmate::assert_numeric(df[[variables$value]])
38	10x	checkmate::assert_numeric(df[[.var]])
39	10x	checkmate::assert_logical(df[[variables$baseline_flag]])
40	10x	checkmate::assert_vector(unique(df[[variables$baseline_flag]]), max.len = 1)
41	10x	assert_df_with_variables(df, c(variables, list(chg = .var)))
42
43	10x	combined <- ifelse(
44	10x	df[[variables$baseline_flag]],
45	10x	df[[variables$value]],
46	10x	df[[.var]]
47		)
48	10x	if (is.logical(combined) && identical(length(combined), 0L)) {
49	1x	combined <- numeric(0)
50		}
51	10x	s_summary(combined, ...)
52		}
53
54		#' @describeIn summarize_change Formatted analysis function which is used as `afun` in `summarize_change()`.
55		#'
56		#' @return
57		#' * `a_change_from_baseline()` returns the corresponding list with formatted [rtables::CellValue()].
58		#'
59		#' @keywords internal
60		a_change_from_baseline <- function(df,
61		...,
62		.stats = NULL,
63		.formats = NULL,
64		.labels = NULL,
65		.indent_mods = NULL) {
66	8x	dots_extra_args <- list(...)
67
68		# Check if there are user-defined functions
69	8x	default_and_custom_stats_list <- .split_std_from_custom_stats(.stats)
70	8x	.stats <- default_and_custom_stats_list$default_stats
71	8x	custom_stat_functions <- default_and_custom_stats_list$custom_stats
72
73		# Adding automatically extra parameters to the statistic function (see ?rtables::additional_fun_params)
74	8x	extra_afun_params <- retrieve_extra_afun_params(
75	8x	names(dots_extra_args$.additional_fun_parameters)
76		)
77	8x	dots_extra_args$.additional_fun_parameters <- NULL # After extraction we do not need them anymore
78
79		# Main stats calculations
80	8x	x_stats <- .apply_stat_functions(
81	8x	default_stat_fnc = s_change_from_baseline,
82	8x	custom_stat_fnc_list = custom_stat_functions,
83	8x	args_list = c(
84	8x	df = list(df),
85	8x	extra_afun_params,
86	8x	dots_extra_args
87		)
88		)
89
90		# Fill in with formatting defaults if needed
91	6x	.stats <- c(
92	6x	get_stats("analyze_vars_numeric", stats_in = .stats),
93	6x	names(custom_stat_functions) # Additional stats from custom functions
94		)
95	6x	.formats <- get_formats_from_stats(.stats, .formats)
96	6x	.labels <- get_labels_from_stats(.stats, .labels)
97	6x	.indent_mods <- get_indents_from_stats(.stats, .indent_mods)
98
99		# Auto format handling
100	6x	.formats <- apply_auto_formatting(
101	6x	.formats,
102	6x	x_stats,
103	6x	extra_afun_params$.df_row,
104	6x	extra_afun_params$.var
105		)
106
107	6x	in_rows(
108	6x	.list = x_stats[.stats],
109	6x	.formats = .formats,
110	6x	.names = names(.labels),
111	6x	.labels = .labels %>% .unlist_keep_nulls(),
112	6x	.indent_mods = .indent_mods %>% .unlist_keep_nulls()
113		)
114		}
115
116		#' @describeIn summarize_change Layout-creating function which can take statistics function arguments
117		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
118		#'
119		#' @return
120		#' * `summarize_change()` returns a layout object suitable for passing to further layouting functions,
121		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
122		#' the statistics from `s_change_from_baseline()` to the table layout.
123		#'
124		#' @note To be used after a split on visits in the layout, such that each data subset only contains
125		#' either baseline or post-baseline data.
126		#'
127		#' @examples
128		#' library(dplyr)
129		#'
130		#' # Fabricate dataset
131		#' dta_test <- data.frame(
132		#' USUBJID = rep(1:6, each = 3),
133		#' AVISIT = rep(paste0("V", 1:3), 6),
134		#' ARM = rep(LETTERS[1:3], rep(6, 3)),
135		#' AVAL = c(9:1, rep(NA, 9))
136		#' ) %>%
137		#' mutate(ABLFLL = AVISIT == "V1") %>%
138		#' group_by(USUBJID) %>%
139		#' mutate(
140		#' BLVAL = AVAL[ABLFLL],
141		#' CHG = AVAL - BLVAL
142		#' ) %>%
143		#' ungroup()
144		#'
145		#' results <- basic_table() %>%
146		#' split_cols_by("ARM") %>%
147		#' split_rows_by("AVISIT") %>%
148		#' summarize_change("CHG", variables = list(value = "AVAL", baseline_flag = "ABLFLL")) %>%
149		#' build_table(dta_test)
150		#'
151		#' results
152		#'
153		#' @export
154		#' @order 2
155		summarize_change <- function(lyt,
156		vars,
157		variables,
158		var_labels = vars,
159		na_str = default_na_str(),
160		na_rm = TRUE,
161		nested = TRUE,
162		show_labels = "default",
163		table_names = vars,
164		section_div = NA_character_,
165		...,
166		.stats = c("n", "mean_sd", "median", "range"),
167		.formats = c(
168		n = "xx",
169		mean_sd = "xx.xx (xx.xx)",
170		mean_se = "xx.xx (xx.xx)",
171		median = "xx.xx",
172		range = "xx.xx - xx.xx",
173		mean_ci = "(xx.xx, xx.xx)",
174		median_ci = "(xx.xx, xx.xx)",
175		mean_pval = "xx.xx"
176		),
177		.labels = c(
178		mean_sd = "Mean (SD)",
179		mean_se = "Mean (SE)",
180		median = "Median",
181		range = "Min - Max"
182		),
183		.indent_mods = NULL) {
184		# Extra args must contain .stats, .formats, .labels, .indent_mods - sent to the analysis level
185	4x	extra_args <- list(".stats" = .stats)
186	4x	if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
187	4x	if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
188	!	if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods
189
190		# Adding additional arguments to the analysis function (depends on the specific call)
191	4x	extra_args <- c(extra_args, "na_rm" = na_rm, "variables" = list(variables), ...)
192
193		# Adding all additional information from layout to analysis functions (see ?rtables::additional_fun_params)
194	4x	extra_args[[".additional_fun_parameters"]] <- get_additional_afun_params(add_alt_df = FALSE)
195	4x	formals(a_change_from_baseline) <- c(
196	4x	formals(a_change_from_baseline),
197	4x	extra_args[[".additional_fun_parameters"]]
198		)
199
200		# Main analysis call - Nothing with .* -> these should be dedicated to the analysis function
201	4x	analyze(
202	4x	lyt = lyt,
203	4x	vars = vars,
204	4x	var_labels = var_labels,
205	4x	afun = a_change_from_baseline,
206	4x	na_str = na_str,
207	4x	nested = nested,
208	4x	extra_args = extra_args,
209	4x	inclNAs = na_rm,
210	4x	show_labels = show_labels,
211	4x	table_names = table_names,
212	4x	section_div = section_div
213		)
214		}

1		#' Odds ratio estimation
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [estimate_odds_ratio()] creates a layout element to compare bivariate responses between
6		#' two groups by estimating an odds ratio and its confidence interval.
7		#'
8		#' The primary analysis variable specified by `vars` is the group variable. Additional variables can be included in the
9		#' analysis via the `variables` argument, which accepts `arm`, an arm variable, and `strata`, a stratification variable.
10		#' If more than two arm levels are present, they can be combined into two groups using the `groups_list` argument.
11		#'
12		#' @inheritParams split_cols_by_groups
13		#' @inheritParams argument_convention
14		#' @param .stats (`character`)\cr statistics to select for the table.
15		#'
16		#' Options are: ``r shQuote(get_stats("estimate_odds_ratio"), type = "sh")``
17		#' @param method (`string`)\cr whether to use the correct (`"exact"`) calculation in the conditional likelihood or one
18		#' of the approximations. See [survival::clogit()] for details.
19		#'
20		#' @note
21		#' * This function uses logistic regression for unstratified analyses, and conditional logistic regression for
22		#' stratified analyses. The Wald confidence interval is calculated with the specified confidence level.
23		#' * For stratified analyses, there is currently no implementation for conditional likelihood confidence intervals,
24		#' therefore the likelihood confidence interval is not available as an option.
25		#' * When `vars` contains only responders or non-responders no odds ratio estimation is possible so the returned
26		#' values will be `NA`.
27		#'
28		#' @seealso Relevant helper function [h_odds_ratio()].
29		#'
30		#' @name odds_ratio
31		#' @order 1
32		NULL
33
34		#' @describeIn odds_ratio Statistics function which estimates the odds ratio
35		#' between a treatment and a control. A `variables` list with `arm` and `strata`
36		#' variable names must be passed if a stratified analysis is required.
37		#'
38		#' @return
39		#' * `s_odds_ratio()` returns a named list with the statistics `or_ci`
40		#' (containing `est`, `lcl`, and `ucl`) and `n_tot`.
41		#'
42		#' @examples
43		#' # Unstratified analysis.
44		#' s_odds_ratio(
45		#' df = subset(dta, grp == "A"),
46		#' .var = "rsp",
47		#' .ref_group = subset(dta, grp == "B"),
48		#' .in_ref_col = FALSE,
49		#' .df_row = dta
50		#' )
51		#'
52		#' # Stratified analysis.
53		#' s_odds_ratio(
54		#' df = subset(dta, grp == "A"),
55		#' .var = "rsp",
56		#' .ref_group = subset(dta, grp == "B"),
57		#' .in_ref_col = FALSE,
58		#' .df_row = dta,
59		#' variables = list(arm = "grp", strata = "strata")
60		#' )
61		#'
62		#' @export
63		s_odds_ratio <- function(df,
64		.var,
65		.ref_group,
66		.in_ref_col,
67		.df_row,
68		variables = list(arm = NULL, strata = NULL),
69		conf_level = 0.95,
70		groups_list = NULL,
71		method = "exact") {
72	87x	y <- list(or_ci = "", n_tot = "")
73
74	87x	if (!.in_ref_col) {
75	87x	assert_proportion_value(conf_level)
76	87x	assert_df_with_variables(df, list(rsp = .var))
77	87x	assert_df_with_variables(.ref_group, list(rsp = .var))
78
79	87x	if (is.null(variables$strata)) {
80	72x	data <- data.frame(
81	72x	rsp = c(.ref_group[[.var]], df[[.var]]),
82	72x	grp = factor(
83	72x	rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
84	72x	levels = c("ref", "Not-ref")
85		)
86		)
87	72x	y <- or_glm(data, conf_level = conf_level)
88		} else {
89	15x	assert_df_with_variables(.df_row, c(list(rsp = .var), variables))
90	15x	checkmate::assert_subset(method, c("exact", "approximate", "efron", "breslow"), empty.ok = FALSE)
91
92		# The group variable prepared for clogit must be synchronised with combination groups definition.
93	15x	if (is.null(groups_list)) {
94	14x	ref_grp <- as.character(unique(.ref_group[[variables$arm]]))
95	14x	trt_grp <- as.character(unique(df[[variables$arm]]))
96	14x	grp <- stats::relevel(factor(.df_row[[variables$arm]]), ref = ref_grp)
97		} else {
98		# If more than one level in reference col.
99	1x	reference <- as.character(unique(.ref_group[[variables$arm]]))
100	1x	grp_ref_flag <- vapply(
101	1x	X = groups_list,
102	1x	FUN.VALUE = TRUE,
103	1x	FUN = function(x) all(reference %in% x)
104		)
105	1x	ref_grp <- names(groups_list)[grp_ref_flag]
106
107		# If more than one level in treatment col.
108	1x	treatment <- as.character(unique(df[[variables$arm]]))
109	1x	grp_trt_flag <- vapply(
110	1x	X = groups_list,
111	1x	FUN.VALUE = TRUE,
112	1x	FUN = function(x) all(treatment %in% x)
113		)
114	1x	trt_grp <- names(groups_list)[grp_trt_flag]
115
116	1x	grp <- combine_levels(.df_row[[variables$arm]], levels = reference, new_level = ref_grp)
117	1x	grp <- combine_levels(grp, levels = treatment, new_level = trt_grp)
118		}
119
120		# The reference level in `grp` must be the same as in the `rtables` column split.
121	15x	data <- data.frame(
122	15x	rsp = .df_row[[.var]],
123	15x	grp = grp,
124	15x	strata = interaction(.df_row[variables$strata])
125		)
126	15x	y_all <- or_clogit(data, conf_level = conf_level, method = method)
127	15x	checkmate::assert_string(trt_grp)
128	15x	checkmate::assert_subset(trt_grp, names(y_all$or_ci))
129	14x	y$or_ci <- y_all$or_ci[[trt_grp]]
130	14x	y$n_tot <- y_all$n_tot
131		}
132		}
133
134	86x	if ("est" %in% names(y$or_ci) && is.na(y$or_ci[["est"]]) && method != "approximate") {
135	1x	warning(
136	1x	"Unable to compute the odds ratio estimate. Please try re-running the function with ",
137	1x	'parameter `method` set to "approximate".'
138		)
139		}
140
141	86x	y$or_ci <- formatters::with_label(
142	86x	x = y$or_ci,
143	86x	label = paste0("Odds Ratio (", 100 * conf_level, "% CI)")
144		)
145
146	86x	y$n_tot <- formatters::with_label(
147	86x	x = y$n_tot,
148	86x	label = "Total n"
149		)
150
151	86x	y
152		}
153
154		#' @describeIn odds_ratio Formatted analysis function which is used as `afun` in `estimate_odds_ratio()`.
155		#'
156		#' @return
157		#' * `a_odds_ratio()` returns the corresponding list with formatted [rtables::CellValue()].
158		#'
159		#' @examples
160		#' a_odds_ratio(
161		#' df = subset(dta, grp == "A"),
162		#' .var = "rsp",
163		#' .ref_group = subset(dta, grp == "B"),
164		#' .in_ref_col = FALSE,
165		#' .df_row = dta
166		#' )
167		#'
168		#' @export
169		a_odds_ratio <- make_afun(
170		s_odds_ratio,
171		.formats = c(or_ci = "xx.xx (xx.xx - xx.xx)"),
172		.indent_mods = c(or_ci = 1L)
173		)
174
175		#' @describeIn odds_ratio Layout-creating function which can take statistics function arguments
176		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
177		#'
178		#' @return
179		#' * `estimate_odds_ratio()` returns a layout object suitable for passing to further layouting functions,
180		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
181		#' the statistics from `s_odds_ratio()` to the table layout.
182		#'
183		#' @examples
184		#' set.seed(12)
185		#' dta <- data.frame(
186		#' rsp = sample(c(TRUE, FALSE), 100, TRUE),
187		#' grp = factor(rep(c("A", "B"), each = 50), levels = c("A", "B")),
188		#' strata = factor(sample(c("C", "D"), 100, TRUE))
189		#' )
190		#'
191		#' l <- basic_table() %>%
192		#' split_cols_by(var = "grp", ref_group = "B") %>%
193		#' estimate_odds_ratio(vars = "rsp")
194		#'
195		#' build_table(l, df = dta)
196		#'
197		#' @export
198		#' @order 2
199		estimate_odds_ratio <- function(lyt,
200		vars,
201		variables = list(arm = NULL, strata = NULL),
202		conf_level = 0.95,
203		groups_list = NULL,
204		na_str = default_na_str(),
205		nested = TRUE,
206		method = "exact",
207		show_labels = "hidden",
208		table_names = vars,
209		var_labels = vars,
210		.stats = "or_ci",
211		.formats = NULL,
212		.labels = NULL,
213		.indent_mods = NULL) {
214	5x	extra_args <- list(variables = variables, conf_level = conf_level, groups_list = groups_list, method = method)
215
216	5x	afun <- make_afun(
217	5x	a_odds_ratio,
218	5x	.stats = .stats,
219	5x	.formats = .formats,
220	5x	.labels = .labels,
221	5x	.indent_mods = .indent_mods
222		)
223
224	5x	analyze(
225	5x	lyt,
226	5x	vars,
227	5x	afun = afun,
228	5x	var_labels = var_labels,
229	5x	na_str = na_str,
230	5x	nested = nested,
231	5x	extra_args = extra_args,
232	5x	show_labels = show_labels,
233	5x	table_names = table_names
234		)
235		}
236
237		#' Helper functions for odds ratio estimation
238		#'
239		#' @description `r lifecycle::badge("stable")`
240		#'
241		#' Functions to calculate odds ratios in [estimate_odds_ratio()].
242		#'
243		#' @inheritParams odds_ratio
244		#' @inheritParams argument_convention
245		#' @param data (`data.frame`)\cr data frame containing at least the variables `rsp` and `grp`, and optionally
246		#' `strata` for [or_clogit()].
247		#'
248		#' @return A named `list` of elements `or_ci` and `n_tot`.
249		#'
250		#' @seealso [odds_ratio]
251		#'
252		#' @name h_odds_ratio
253		NULL
254
255		#' @describeIn h_odds_ratio Estimates the odds ratio based on [stats::glm()]. Note that there must be
256		#' exactly 2 groups in `data` as specified by the `grp` variable.
257		#'
258		#' @examples
259		#' # Data with 2 groups.
260		#' data <- data.frame(
261		#' rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1)),
262		#' grp = letters[c(1, 1, 1, 2, 2, 2, 1, 2)],
263		#' strata = letters[c(1, 2, 1, 2, 2, 2, 1, 2)],
264		#' stringsAsFactors = TRUE
265		#' )
266		#'
267		#' # Odds ratio based on glm.
268		#' or_glm(data, conf_level = 0.95)
269		#'
270		#' @export
271		or_glm <- function(data, conf_level) {
272	77x	checkmate::assert_logical(data$rsp)
273	77x	assert_proportion_value(conf_level)
274	77x	assert_df_with_variables(data, list(rsp = "rsp", grp = "grp"))
275	77x	checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))
276
277	77x	data$grp <- as_factor_keep_attributes(data$grp)
278	77x	assert_df_with_factors(data, list(val = "grp"), min.levels = 2, max.levels = 2)
279	77x	formula <- stats::as.formula("rsp ~ grp")
280	77x	model_fit <- stats::glm(
281	77x	formula = formula, data = data,
282	77x	family = stats::binomial(link = "logit")
283		)
284
285		# Note that here we need to discard the intercept.
286	77x	or <- exp(stats::coef(model_fit)[-1])
287	77x	or_ci <- exp(
288	77x	stats::confint.default(model_fit, level = conf_level)[-1, , drop = FALSE]
289		)
290
291	77x	values <- stats::setNames(c(or, or_ci), c("est", "lcl", "ucl"))
292	77x	n_tot <- stats::setNames(nrow(model_fit$model), "n_tot")
293
294	77x	list(or_ci = values, n_tot = n_tot)
295		}
296
297		#' @describeIn h_odds_ratio Estimates the odds ratio based on [survival::clogit()]. This is done for
298		#' the whole data set including all groups, since the results are not the same as when doing
299		#' pairwise comparisons between the groups.
300		#'
301		#' @examples
302		#' # Data with 3 groups.
303		#' data <- data.frame(
304		#' rsp = as.logical(c(1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0)),
305		#' grp = letters[c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3)],
306		#' strata = LETTERS[c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)],
307		#' stringsAsFactors = TRUE
308		#' )
309		#'
310		#' # Odds ratio based on stratified estimation by conditional logistic regression.
311		#' or_clogit(data, conf_level = 0.95)
312		#'
313		#' @export
314		or_clogit <- function(data, conf_level, method = "exact") {
315	19x	checkmate::assert_logical(data$rsp)
316	19x	assert_proportion_value(conf_level)
317	19x	assert_df_with_variables(data, list(rsp = "rsp", grp = "grp", strata = "strata"))
318	19x	checkmate::assert_multi_class(data$grp, classes = c("factor", "character"))
319	19x	checkmate::assert_multi_class(data$strata, classes = c("factor", "character"))
320	19x	checkmate::assert_subset(method, c("exact", "approximate", "efron", "breslow"), empty.ok = FALSE)
321
322	19x	data$grp <- as_factor_keep_attributes(data$grp)
323	19x	data$strata <- as_factor_keep_attributes(data$strata)
324
325		# Deviation from convention: `survival::strata` must be simply `strata`.
326	19x	formula <- stats::as.formula("rsp ~ grp + strata(strata)")
327	19x	model_fit <- clogit_with_tryCatch(formula = formula, data = data, method = method)
328
329		# Create a list with one set of OR estimates and CI per coefficient, i.e.
330		# comparison of one group vs. the reference group.
331	19x	coef_est <- stats::coef(model_fit)
332	19x	ci_est <- stats::confint(model_fit, level = conf_level)
333	19x	or_ci <- list()
334	19x	for (coef_name in names(coef_est)) {
335	21x	grp_name <- gsub("^grp", "", x = coef_name)
336	21x	or_ci[[grp_name]] <- stats::setNames(
337	21x	object = exp(c(coef_est[coef_name], ci_est[coef_name, , drop = TRUE])),
338	21x	nm = c("est", "lcl", "ucl")
339		)
340		}
341	19x	list(or_ci = or_ci, n_tot = c(n_tot = model_fit$n))
342		}

1		#' Helper functions for subgroup treatment effect pattern (STEP) calculations
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions that are used internally for the STEP calculations.
6		#'
7		#' @inheritParams argument_convention
8		#'
9		#' @name h_step
10		#' @include control_step.R
11		NULL
12
13		#' @describeIn h_step Creates the windows for STEP, based on the control settings
14		#' provided.
15		#'
16		#' @param x (`numeric`)\cr biomarker value(s) to use (without `NA`).
17		#' @param control (named `list`)\cr output from `control_step()`.
18		#'
19		#' @return
20		#' * `h_step_window()` returns a list containing the window-selection matrix `sel`
21		#' and the interval information matrix `interval`.
22		#'
23		#' @export
24		h_step_window <- function(x,
25		control = control_step()) {
26	12x	checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
27	12x	checkmate::assert_list(control, names = "named")
28
29	12x	sel <- matrix(FALSE, length(x), control$num_points)
30	12x	out <- matrix(0, control$num_points, 3)
31	12x	colnames(out) <- paste("Interval", c("Center", "Lower", "Upper"))
32	12x	if (control$use_percentile) {
33		# Create windows according to percentile cutoffs.
34	9x	out <- cbind(out, out)
35	9x	colnames(out)[1:3] <- paste("Percentile", c("Center", "Lower", "Upper"))
36	9x	xs <- seq(0, 1, length.out = control$num_points + 2)[-1]
37	9x	for (i in seq_len(control$num_points)) {
38	185x	out[i, 2:3] <- c(
39	185x	max(xs[i] - control$bandwidth, 0),
40	185x	min(xs[i] + control$bandwidth, 1)
41		)
42	185x	out[i, 5:6] <- stats::quantile(x, out[i, 2:3])
43	185x	sel[, i] <- x >= out[i, 5] & x <= out[i, 6]
44		}
45		# Center is the middle point of the percentile window.
46	9x	out[, 1] <- xs[-control$num_points - 1]
47	9x	out[, 4] <- stats::quantile(x, out[, 1])
48		} else {
49		# Create windows according to cutoffs.
50	3x	m <- c(min(x), max(x))
51	3x	xs <- seq(m[1], m[2], length.out = control$num_points + 2)[-1]
52	3x	for (i in seq_len(control$num_points)) {
53	11x	out[i, 2:3] <- c(
54	11x	max(xs[i] - control$bandwidth, m[1]),
55	11x	min(xs[i] + control$bandwidth, m[2])
56		)
57	11x	sel[, i] <- x >= out[i, 2] & x <= out[i, 3]
58		}
59		# Center is the same as the point for predicting.
60	3x	out[, 1] <- xs[-control$num_points - 1]
61		}
62	12x	list(sel = sel, interval = out)
63		}
64
65		#' @describeIn h_step Calculates the estimated treatment effect estimate
66		#' on the linear predictor scale and corresponding standard error from a STEP `model` fitted
67		#' on `data` given `variables` specification, for a single biomarker value `x`.
68		#' This works for both `coxph` and `glm` models, i.e. for calculating log hazard ratio or log odds
69		#' ratio estimates.
70		#'
71		#' @param model (`coxph` or `glm`)\cr the regression model object.
72		#'
73		#' @return
74		#' * `h_step_trt_effect()` returns a vector with elements `est` and `se`.
75		#'
76		#' @export
77		h_step_trt_effect <- function(data,
78		model,
79		variables,
80		x) {
81	208x	checkmate::assert_multi_class(model, c("coxph", "glm"))
82	208x	checkmate::assert_number(x)
83	208x	assert_df_with_variables(data, variables)
84	208x	checkmate::assert_factor(data[[variables$arm]], n.levels = 2)
85
86	208x	newdata <- data[c(1, 1), ]
87	208x	newdata[, variables$biomarker] <- x
88	208x	newdata[, variables$arm] <- levels(data[[variables$arm]])
89	208x	model_terms <- stats::delete.response(stats::terms(model))
90	208x	model_frame <- stats::model.frame(model_terms, data = newdata, xlev = model$xlevels)
91	208x	mat <- stats::model.matrix(model_terms, data = model_frame, contrasts.arg = model$contrasts)
92	208x	coefs <- stats::coef(model)
93		# Note: It is important to use the coef subset from matrix, otherwise intercept and
94		# strata are included for coxph() models.
95	208x	mat <- mat[, names(coefs)]
96	208x	mat_diff <- diff(mat)
97	208x	est <- mat_diff %*% coefs
98	208x	var <- mat_diff %% stats::vcov(model) %% t(mat_diff)
99	208x	se <- sqrt(var)
100	208x	c(
101	208x	est = est,
102	208x	se = se
103		)
104		}
105
106		#' @describeIn h_step Builds the model formula used in survival STEP calculations.
107		#'
108		#' @return
109		#' * `h_step_survival_formula()` returns a model formula.
110		#'
111		#' @export
112		h_step_survival_formula <- function(variables,
113		control = control_step()) {
114	10x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
115
116	10x	assert_list_of_variables(variables[c("arm", "biomarker", "event", "time")])
117	10x	form <- paste0("Surv(", variables$time, ", ", variables$event, ") ~ ", variables$arm)
118	10x	if (control$degree > 0) {
119	5x	form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
120		}
121	10x	if (!is.null(variables$covariates)) {
122	6x	form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
123		}
124	10x	if (!is.null(variables$strata)) {
125	2x	form <- paste0(form, " + strata(", paste0(variables$strata, collapse = ", "), ")")
126		}
127	10x	stats::as.formula(form)
128		}
129
130		#' @describeIn h_step Estimates the model with `formula` built based on
131		#' `variables` in `data` for a given `subset` and `control` parameters for the
132		#' Cox regression.
133		#'
134		#' @param formula (`formula`)\cr the regression model formula.
135		#' @param subset (`logical`)\cr subset vector.
136		#'
137		#' @return
138		#' * `h_step_survival_est()` returns a matrix of number of observations `n`,
139		#' `events`, log hazard ratio estimates `loghr`, standard error `se`,
140		#' and Wald confidence interval bounds `ci_lower` and `ci_upper`. One row is
141		#' included for each biomarker value in `x`.
142		#'
143		#' @export
144		h_step_survival_est <- function(formula,
145		data,
146		variables,
147		x,
148		subset = rep(TRUE, nrow(data)),
149		control = control_coxph()) {
150	55x	checkmate::assert_formula(formula)
151	55x	assert_df_with_variables(data, variables)
152	55x	checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
153	55x	checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
154	55x	checkmate::assert_list(control, names = "named")
155
156		# Note: `subset` in `coxph` needs to be an expression referring to `data` variables.
157	55x	data$.subset <- subset
158	55x	coxph_warnings <- NULL
159	55x	tryCatch(
160	55x	withCallingHandlers(
161	55x	expr = {
162	55x	fit <- survival::coxph(
163	55x	formula = formula,
164	55x	data = data,
165	55x	subset = .subset,
166	55x	ties = control$ties
167		)
168		},
169	55x	warning = function(w) {
170	1x	coxph_warnings <<- c(coxph_warnings, w)
171	1x	invokeRestart("muffleWarning")
172		}
173		),
174	55x	finally = {
175		}
176		)
177	55x	if (!is.null(coxph_warnings)) {
178	1x	warning(paste(
179	1x	"Fit warnings occurred, please consider using a simpler model, or",
180	1x	"larger `bandwidth`, less `num_points` in `control_step()` settings"
181		))
182		}
183		# Produce a matrix with one row per `x` and columns `est` and `se`.
184	55x	estimates <- t(vapply(
185	55x	X = x,
186	55x	FUN = h_step_trt_effect,
187	55x	FUN.VALUE = c(1, 2),
188	55x	data = data,
189	55x	model = fit,
190	55x	variables = variables
191		))
192	55x	q_norm <- stats::qnorm((1 + control$conf_level) / 2)
193	55x	cbind(
194	55x	n = fit$n,
195	55x	events = fit$nevent,
196	55x	loghr = estimates[, "est"],
197	55x	se = estimates[, "se"],
198	55x	ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
199	55x	ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
200		)
201		}
202
203		#' @describeIn h_step Builds the model formula used in response STEP calculations.
204		#'
205		#' @return
206		#' * `h_step_rsp_formula()` returns a model formula.
207		#'
208		#' @export
209		h_step_rsp_formula <- function(variables,
210		control = c(control_step(), control_logistic())) {
211	14x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
212	14x	assert_list_of_variables(variables[c("arm", "biomarker", "response")])
213	14x	response_definition <- sub(
214	14x	pattern = "response",
215	14x	replacement = variables$response,
216	14x	x = control$response_definition,
217	14x	fixed = TRUE
218		)
219	14x	form <- paste0(response_definition, " ~ ", variables$arm)
220	14x	if (control$degree > 0) {
221	8x	form <- paste0(form, " * stats::poly(", variables$biomarker, ", degree = ", control$degree, ", raw = TRUE)")
222		}
223	14x	if (!is.null(variables$covariates)) {
224	8x	form <- paste(form, "+", paste(variables$covariates, collapse = "+"))
225		}
226	14x	if (!is.null(variables$strata)) {
227	5x	strata_arg <- if (length(variables$strata) > 1) {
228	2x	paste0("I(interaction(", paste0(variables$strata, collapse = ", "), "))")
229		} else {
230	3x	variables$strata
231		}
232	5x	form <- paste0(form, "+ strata(", strata_arg, ")")
233		}
234	14x	stats::as.formula(form)
235		}
236
237		#' @describeIn h_step Estimates the model with `formula` built based on
238		#' `variables` in `data` for a given `subset` and `control` parameters for the
239		#' logistic regression.
240		#'
241		#' @param formula (`formula`)\cr the regression model formula.
242		#' @param subset (`logical`)\cr subset vector.
243		#'
244		#' @return
245		#' * `h_step_rsp_est()` returns a matrix of number of observations `n`, log odds
246		#' ratio estimates `logor`, standard error `se`, and Wald confidence interval bounds
247		#' `ci_lower` and `ci_upper`. One row is included for each biomarker value in `x`.
248		#'
249		#' @export
250		h_step_rsp_est <- function(formula,
251		data,
252		variables,
253		x,
254		subset = rep(TRUE, nrow(data)),
255		control = control_logistic()) {
256	58x	checkmate::assert_formula(formula)
257	58x	assert_df_with_variables(data, variables)
258	58x	checkmate::assert_logical(subset, min.len = 1, any.missing = FALSE)
259	58x	checkmate::assert_numeric(x, min.len = 1, any.missing = FALSE)
260	58x	checkmate::assert_list(control, names = "named")
261		# Note: `subset` in `glm` needs to be an expression referring to `data` variables.
262	58x	data$.subset <- subset
263	58x	fit_warnings <- NULL
264	58x	tryCatch(
265	58x	withCallingHandlers(
266	58x	expr = {
267	58x	fit <- if (is.null(variables$strata)) {
268	54x	stats::glm(
269	54x	formula = formula,
270	54x	data = data,
271	54x	subset = .subset,
272	54x	family = stats::binomial("logit")
273		)
274		} else {
275		# clogit needs coxph and strata imported
276	4x	survival::clogit(
277	4x	formula = formula,
278	4x	data = data,
279	4x	subset = .subset
280		)
281		}
282		},
283	58x	warning = function(w) {
284	19x	fit_warnings <<- c(fit_warnings, w)
285	19x	invokeRestart("muffleWarning")
286		}
287		),
288	58x	finally = {
289		}
290		)
291	58x	if (!is.null(fit_warnings)) {
292	13x	warning(paste(
293	13x	"Fit warnings occurred, please consider using a simpler model, or",
294	13x	"larger `bandwidth`, less `num_points` in `control_step()` settings"
295		))
296		}
297		# Produce a matrix with one row per `x` and columns `est` and `se`.
298	58x	estimates <- t(vapply(
299	58x	X = x,
300	58x	FUN = h_step_trt_effect,
301	58x	FUN.VALUE = c(1, 2),
302	58x	data = data,
303	58x	model = fit,
304	58x	variables = variables
305		))
306	58x	q_norm <- stats::qnorm((1 + control$conf_level) / 2)
307	58x	cbind(
308	58x	n = length(fit$y),
309	58x	logor = estimates[, "est"],
310	58x	se = estimates[, "se"],
311	58x	ci_lower = estimates[, "est"] - q_norm * estimates[, "se"],
312	58x	ci_upper = estimates[, "est"] + q_norm * estimates[, "se"]
313		)
314		}

1		#' Convert `rtable` objects to `ggplot` objects
2		#'
3		#' @description `r lifecycle::badge("experimental")`
4		#'
5		#' Given a [rtables::rtable()] object, performs basic conversion to a [ggplot2::ggplot()] object built using
6		#' functions from the `ggplot2` package. Any table titles and/or footnotes are ignored.
7		#'
8		#' @param tbl (`VTableTree`)\cr `rtables` table object.
9		#' @param fontsize (`numeric(1)`)\cr font size.
10		#' @param colwidths (`numeric` or `NULL`)\cr a vector of column widths. Each element's position in
11		#' `colwidths` corresponds to the column of `tbl` in the same position. If `NULL`, column widths
12		#' are calculated according to maximum number of characters per column.
13		#' @param lbl_col_padding (`numeric`)\cr additional padding to use when calculating spacing between
14		#' the first (label) column and the second column of `tbl`. If `colwidths` is specified,
15		#' the width of the first column becomes `colwidths[1] + lbl_col_padding`. Defaults to 0.
16		#'
17		#' @return A `ggplot` object.
18		#'
19		#' @examples
20		#' dta <- data.frame(
21		#' ARM = rep(LETTERS[1:3], rep(6, 3)),
22		#' AVISIT = rep(paste0("V", 1:3), 6),
23		#' AVAL = c(9:1, rep(NA, 9))
24		#' )
25		#'
26		#' lyt <- basic_table() %>%
27		#' split_cols_by(var = "ARM") %>%
28		#' split_rows_by(var = "AVISIT") %>%
29		#' analyze_vars(vars = "AVAL")
30		#'
31		#' tbl <- build_table(lyt, df = dta)
32		#'
33		#' rtable2gg(tbl)
34		#'
35		#' rtable2gg(tbl, fontsize = 15, colwidths = c(2, 1, 1, 1))
36		#'
37		#' @export
38		rtable2gg <- function(tbl, fontsize = 12, colwidths = NULL, lbl_col_padding = 0) {
39	6x	mat <- rtables::matrix_form(tbl, indent_rownames = TRUE)
40	6x	mat_strings <- formatters::mf_strings(mat)
41	6x	mat_aligns <- formatters::mf_aligns(mat)
42	6x	mat_indent <- formatters::mf_rinfo(mat)$indent
43	6x	mat_display <- formatters::mf_display(mat)
44	6x	nlines_hdr <- formatters::mf_nlheader(mat)
45	6x	shared_hdr_rows <- which(apply(mat_display, 1, function(x) (any(!x))))
46
47	6x	tbl_df <- data.frame(mat_strings)
48	6x	body_rows <- seq(nlines_hdr + 1, nrow(tbl_df))
49	6x	mat_aligns <- apply(mat_aligns, 1:2, function(x) if (x == "left") 0 else if (x == "right") 1 else 0.5)
50
51		# Apply indentation in first column
52	6x	tbl_df[body_rows, 1] <- sapply(body_rows, function(i) {
53	42x	ind_i <- mat_indent[i - nlines_hdr] * 4
54	18x	if (ind_i > 0) paste0(paste(rep(" ", ind_i), collapse = ""), tbl_df[i, 1]) else tbl_df[i, 1]
55		})
56
57		# Get column widths
58	6x	if (is.null(colwidths)) {
59	6x	colwidths <- apply(tbl_df, 2, function(x) max(nchar(x))) + 1
60		}
61	6x	tot_width <- sum(colwidths) + lbl_col_padding
62
63	6x	if (length(shared_hdr_rows) > 0) {
64	5x	tbl_df <- tbl_df[-shared_hdr_rows, ]
65	5x	mat_aligns <- mat_aligns[-shared_hdr_rows, ]
66		}
67
68	6x	res <- ggplot(data = tbl_df) +
69	6x	theme_void() +
70	6x	scale_x_continuous(limits = c(0, tot_width)) +
71	6x	scale_y_continuous(limits = c(0, nrow(mat_strings))) +
72	6x	annotate(
73	6x	"segment",
74	6x	x = 0, xend = tot_width,
75	6x	y = nrow(mat_strings) - nlines_hdr + 0.5, yend = nrow(mat_strings) - nlines_hdr + 0.5
76		)
77
78		# If header content spans multiple columns, center over these columns
79	6x	if (length(shared_hdr_rows) > 0) {
80	5x	mat_strings[shared_hdr_rows, ] <- trimws(mat_strings[shared_hdr_rows, ])
81	5x	for (hr in shared_hdr_rows) {
82	6x	hdr_lbls <- mat_strings[1:hr, mat_display[hr, -1]]
83	6x	hdr_lbls <- matrix(hdr_lbls[nzchar(hdr_lbls)], nrow = hr)
84	6x	for (idx_hl in seq_len(ncol(hdr_lbls))) {
85	13x	cur_lbl <- tail(hdr_lbls[, idx_hl], 1)
86	13x	which_cols <- if (hr == 1) {
87	9x	which(mat_strings[hr, ] == hdr_lbls[idx_hl])
88	13x	} else { # for >2 col splits, only print labels for each unique combo of nested columns
89	4x	which(
90	4x	apply(mat_strings[1:hr, ], 2, function(x) all(x == hdr_lbls[1:hr, idx_hl]))
91		)
92		}
93	13x	line_pos <- c(
94	13x	sum(colwidths[1:(which_cols[1] - 1)]) + 1 + lbl_col_padding,
95	13x	sum(colwidths[1:max(which_cols)]) - 1 + lbl_col_padding
96		)
97
98	13x	res <- res +
99	13x	annotate(
100	13x	"text",
101	13x	x = mean(line_pos),
102	13x	y = nrow(mat_strings) + 1 - hr,
103	13x	label = cur_lbl,
104	13x	size = fontsize / .pt
105		) +
106	13x	annotate(
107	13x	"segment",
108	13x	x = line_pos[1],
109	13x	xend = line_pos[2],
110	13x	y = nrow(mat_strings) - hr + 0.5,
111	13x	yend = nrow(mat_strings) - hr + 0.5
112		)
113		}
114		}
115		}
116
117		# Add table columns
118	6x	for (i in seq_len(ncol(tbl_df))) {
119	40x	res <- res + annotate(
120	40x	"text",
121	40x	x = if (i == 1) 0 else sum(colwidths[1:i]) - 0.5 * colwidths[i] + lbl_col_padding,
122	40x	y = rev(seq_len(nrow(tbl_df))),
123	40x	label = tbl_df[, i],
124	40x	hjust = mat_aligns[, i],
125	40x	size = fontsize / .pt
126		)
127		}
128
129	6x	res
130		}
131
132		#' Convert `data.frame` object to `ggplot` object
133		#'
134		#' @description `r lifecycle::badge("experimental")`
135		#'
136		#' Given a `data.frame` object, performs basic conversion to a [ggplot2::ggplot()] object built using
137		#' functions from the `ggplot2` package.
138		#'
139		#' @param df (`data.frame`)\cr a data frame.
140		#' @param colwidths (`numeric` or `NULL`)\cr a vector of column widths. Each element's position in
141		#' `colwidths` corresponds to the column of `df` in the same position. If `NULL`, column widths
142		#' are calculated according to maximum number of characters per column.
143		#' @param font_size (`numeric(1)`)\cr font size.
144		#' @param col_labels (`flag`)\cr whether the column names (labels) of `df` should be used as the first row
145		#' of the output table.
146		#' @param col_lab_fontface (`string`)\cr font face to apply to the first row (of column labels
147		#' if `col_labels = TRUE`). Defaults to `"bold"`.
148		#' @param hline (`flag`)\cr whether a horizontal line should be printed below the first row of the table.
149		#' @param bg_fill (`string`)\cr table background fill color.
150		#'
151		#' @return A `ggplot` object.
152		#'
153		#' @examples
154		#' \dontrun{
155		#' df2gg(head(iris, 5))
156		#'
157		#' df2gg(head(iris, 5), font_size = 15, colwidths = c(1, 1, 1, 1, 1))
158		#' }
159		#' @keywords internal
160		df2gg <- function(df,
161		colwidths = NULL,
162		font_size = 10,
163		col_labels = TRUE,
164		col_lab_fontface = "bold",
165		hline = TRUE,
166		bg_fill = NULL) {
167		# convert to text
168	19x	df <- as.data.frame(apply(df, 1:2, function(x) if (is.na(x)) "NA" else as.character(x)))
169
170	19x	if (col_labels) {
171	10x	df <- as.matrix(df)
172	10x	df <- rbind(colnames(df), df)
173		}
174
175		# Get column widths
176	19x	if (is.null(colwidths)) {
177	1x	colwidths <- apply(df, 2, function(x) max(nchar(x), na.rm = TRUE))
178		}
179	19x	tot_width <- sum(colwidths)
180
181	19x	res <- ggplot(data = df) +
182	19x	theme_void() +
183	19x	scale_x_continuous(limits = c(0, tot_width)) +
184	19x	scale_y_continuous(limits = c(1, nrow(df)))
185
186	9x	if (!is.null(bg_fill)) res <- res + theme(plot.background = element_rect(fill = bg_fill))
187
188	19x	if (hline) {
189	10x	res <- res +
190	10x	annotate(
191	10x	"segment",
192	10x	x = 0 + 0.2 * colwidths[2], xend = tot_width - 0.1 * tail(colwidths, 1),
193	10x	y = nrow(df) - 0.5, yend = nrow(df) - 0.5
194		)
195		}
196
197	19x	for (i in seq_len(ncol(df))) {
198	86x	line_pos <- c(
199	86x	if (i == 1) 0 else sum(colwidths[1:(i - 1)]),
200	86x	sum(colwidths[1:i])
201		)
202	86x	res <- res +
203	86x	annotate(
204	86x	"text",
205	86x	x = mean(line_pos),
206	86x	y = rev(seq_len(nrow(df))),
207	86x	label = df[, i],
208	86x	size = font_size / .pt,
209	86x	fontface = if (col_labels) {
210	32x	c(col_lab_fontface, rep("plain", nrow(df) - 1))
211		} else {
212	54x	rep("plain", nrow(df))
213		}
214		)
215		}
216
217	19x	res
218		}

1		#' Cumulative counts of numeric variable by thresholds
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [count_cumulative()] creates a layout element to calculate cumulative counts of values in a
6		#' numeric variable that are less than, less or equal to, greater than, or greater or equal to user-specified
7		#' threshold values.
8		#'
9		#' This function analyzes numeric variable `vars` against the threshold values supplied to the `thresholds`
10		#' argument as a numeric vector. Whether counts should include the threshold values, and whether to count
11		#' values lower or higher than the threshold values can be set via the `include_eq` and `lower_tail`
12		#' parameters, respectively.
13		#'
14		#' @inheritParams h_count_cumulative
15		#' @inheritParams argument_convention
16		#' @param thresholds (`numeric`)\cr vector of cutoff values for the counts.
17		#' @param .stats (`character`)\cr statistics to select for the table.
18		#'
19		#' Options are: ``r shQuote(get_stats("count_cumulative"), type = "sh")``
20		#'
21		#' @seealso Relevant helper function [h_count_cumulative()], and descriptive function [d_count_cumulative()].
22		#'
23		#' @name count_cumulative
24		#' @order 1
25		NULL
26
27		#' Helper function for `s_count_cumulative()`
28		#'
29		#' @description `r lifecycle::badge("stable")`
30		#'
31		#' Helper function to calculate count and fraction of `x` values in the lower or upper tail given a threshold.
32		#'
33		#' @inheritParams argument_convention
34		#' @param threshold (`numeric(1)`)\cr a cutoff value as threshold to count values of `x`.
35		#' @param lower_tail (`flag`)\cr whether to count lower tail, default is `TRUE`.
36		#' @param include_eq (`flag`)\cr whether to include value equal to the `threshold` in
37		#' count, default is `TRUE`.
38		#'
39		#' @return A named vector with items:
40		#' * `count`: the count of values less than, less or equal to, greater than, or greater or equal to a threshold
41		#' of user specification.
42		#' * `fraction`: the fraction of the count.
43		#'
44		#' @seealso [count_cumulative]
45		#'
46		#' @examples
47		#' set.seed(1, kind = "Mersenne-Twister")
48		#' x <- c(sample(1:10, 10), NA)
49		#' .N_col <- length(x)
50		#'
51		#' h_count_cumulative(x, 5, .N_col = .N_col)
52		#' h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col)
53		#' h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col)
54		#' h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col)
55		#'
56		#' @export
57		h_count_cumulative <- function(x,
58		threshold,
59		lower_tail = TRUE,
60		include_eq = TRUE,
61		na.rm = TRUE, # nolint
62		.N_col) { # nolint
63	36x	checkmate::assert_numeric(x)
64	36x	checkmate::assert_numeric(threshold)
65	36x	checkmate::assert_numeric(.N_col)
66	36x	checkmate::assert_flag(lower_tail)
67	36x	checkmate::assert_flag(include_eq)
68	36x	checkmate::assert_flag(na.rm)
69
70	36x	is_keep <- if (na.rm) !is.na(x) else rep(TRUE, length(x))
71	36x	count <- if (lower_tail && include_eq) {
72	7x	length(x[is_keep & x <= threshold])
73	36x	} else if (lower_tail && !include_eq) {
74	!	length(x[is_keep & x < threshold])
75	36x	} else if (!lower_tail && include_eq) {
76	14x	length(x[is_keep & x >= threshold])
77	36x	} else if (!lower_tail && !include_eq) {
78	15x	length(x[is_keep & x > threshold])
79		}
80
81	36x	result <- c(
82	36x	count = count,
83	36x	fraction = if (count == 0 && .N_col == 0) 0 else count / .N_col
84		)
85	36x	result
86		}
87
88		#' Description of cumulative count
89		#'
90		#' @description `r lifecycle::badge("stable")`
91		#'
92		#' This is a helper function that describes the analysis in [s_count_cumulative()].
93		#'
94		#' @inheritParams h_count_cumulative
95		#'
96		#' @return Labels for [s_count_cumulative()].
97		#'
98		#' @export
99		d_count_cumulative <- function(threshold, lower_tail = TRUE, include_eq = TRUE) {
100	34x	checkmate::assert_numeric(threshold)
101	34x	lg <- if (lower_tail) "<" else ">"
102	34x	eq <- if (include_eq) "=" else ""
103	34x	paste0(lg, eq, " ", threshold)
104		}
105
106		#' @describeIn count_cumulative Statistics function that produces a named list given a numeric vector of thresholds.
107		#'
108		#' @return
109		#' * `s_count_cumulative()` returns a named list of `count_fraction`s: a list with each `thresholds` value as a
110		#' component, each component containing a vector for the count and fraction.
111		#'
112		#' @keywords internal
113		s_count_cumulative <- function(x,
114		thresholds,
115		lower_tail = TRUE,
116		include_eq = TRUE,
117		.N_col, # nolint
118		.N_row, # nolint
119		denom = c("N_col", "n", "N_row"),
120		...) {
121	9x	checkmate::assert_numeric(thresholds, min.len = 1, any.missing = FALSE)
122
123	9x	denom <- match.arg(denom) %>%
124	9x	switch(
125	9x	n = length(x),
126	9x	N_row = .N_row,
127	9x	N_col = .N_col
128		)
129
130	9x	count_fraction_list <- Map(function(thres) {
131	18x	result <- h_count_cumulative(x, thres, lower_tail, include_eq, .N_col = denom, ...)
132	18x	label <- d_count_cumulative(thres, lower_tail, include_eq)
133	18x	formatters::with_label(result, label)
134	9x	}, thresholds)
135
136	9x	names(count_fraction_list) <- thresholds
137	9x	list(count_fraction = count_fraction_list)
138		}
139
140		#' @describeIn count_cumulative Formatted analysis function which is used as `afun`
141		#' in `count_cumulative()`.
142		#'
143		#' @return
144		#' * `a_count_cumulative()` returns the corresponding list with formatted [rtables::CellValue()].
145		#'
146		#' @keywords internal
147		a_count_cumulative <- make_afun(
148		s_count_cumulative,
149		.formats = c(count_fraction = format_count_fraction)
150		)
151
152		#' @describeIn count_cumulative Layout-creating function which can take statistics function arguments
153		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
154		#'
155		#' @return
156		#' * `count_cumulative()` returns a layout object suitable for passing to further layouting functions,
157		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
158		#' the statistics from `s_count_cumulative()` to the table layout.
159		#'
160		#' @examples
161		#' basic_table() %>%
162		#' split_cols_by("ARM") %>%
163		#' add_colcounts() %>%
164		#' count_cumulative(
165		#' vars = "AGE",
166		#' thresholds = c(40, 60)
167		#' ) %>%
168		#' build_table(tern_ex_adsl)
169		#'
170		#' @export
171		#' @order 2
172		count_cumulative <- function(lyt,
173		vars,
174		thresholds,
175		lower_tail = TRUE,
176		include_eq = TRUE,
177		var_labels = vars,
178		show_labels = "visible",
179		na_str = default_na_str(),
180		nested = TRUE,
181		...,
182		table_names = vars,
183		.stats = NULL,
184		.formats = NULL,
185		.labels = NULL,
186		.indent_mods = NULL) {
187	3x	extra_args <- list(thresholds = thresholds, lower_tail = lower_tail, include_eq = include_eq, ...)
188
189	3x	afun <- make_afun(
190	3x	a_count_cumulative,
191	3x	.stats = .stats,
192	3x	.formats = .formats,
193	3x	.labels = .labels,
194	3x	.indent_mods = .indent_mods,
195	3x	.ungroup_stats = "count_fraction"
196		)
197	3x	analyze(
198	3x	lyt,
199	3x	vars,
200	3x	afun = afun,
201	3x	na_str = na_str,
202	3x	table_names = table_names,
203	3x	var_labels = var_labels,
204	3x	show_labels = show_labels,
205	3x	nested = nested,
206	3x	extra_args = extra_args
207		)
208		}

1		#' Count number of patients and sum exposure across all patients in columns
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [analyze_patients_exposure_in_cols()] creates a layout element to count total numbers of
6		#' patients and sum an analysis value (i.e. exposure) across all patients in columns.
7		#'
8		#' The primary analysis variable `ex_var` is the exposure variable used to calculate the `sum_exposure` statistic. The
9		#' `id` variable is used to uniquely identify patients in the data such that only unique patients are counted in the
10		#' `n_patients` statistic, and the `var` variable is used to create a row split if needed. The percentage returned as
11		#' part of the `n_patients` statistic is the proportion of all records that correspond to a unique patient.
12		#'
13		#' The summarize function [summarize_patients_exposure_in_cols()] performs the same function as
14		#' [analyze_patients_exposure_in_cols()] except it creates content rows, not data rows, to summarize the current table
15		#' row/column context and operates on the level of the latest row split or the root of the table if no row splits have
16		#' occurred.
17		#'
18		#' If a column split has not yet been performed in the table, `col_split` must be set to `TRUE` for the first call of
19		#' [analyze_patients_exposure_in_cols()] or [summarize_patients_exposure_in_cols()].
20		#'
21		#' @inheritParams argument_convention
22		#' @param ex_var (`string`)\cr name of the variable in `df` containing exposure values.
23		#' @param custom_label (`string` or `NULL`)\cr if provided and `labelstr` is empty, this will be used as label.
24		#' @param .stats (`character`)\cr statistics to select for the table.
25		#'
26		#' Options are: ``r shQuote(get_stats("analyze_patients_exposure_in_cols"), type = "sh")``
27		#'
28		#' @name summarize_patients_exposure_in_cols
29		#' @order 1
30		NULL
31
32		#' @describeIn summarize_patients_exposure_in_cols Statistics function which counts numbers
33		#' of patients and the sum of exposure across all patients.
34		#'
35		#' @return
36		#' * `s_count_patients_sum_exposure()` returns a named `list` with the statistics:
37		#' * `n_patients`: Number of unique patients in `df`.
38		#' * `sum_exposure`: Sum of `ex_var` across all patients in `df`.
39		#'
40		#' @keywords internal
41		s_count_patients_sum_exposure <- function(df,
42		ex_var = "AVAL",
43		id = "USUBJID",
44		labelstr = "",
45		.stats = c("n_patients", "sum_exposure"),
46		.N_col, # nolint
47		custom_label = NULL) {
48	56x	assert_df_with_variables(df, list(ex_var = ex_var, id = id))
49	56x	checkmate::assert_string(id)
50	56x	checkmate::assert_string(labelstr)
51	56x	checkmate::assert_string(custom_label, null.ok = TRUE)
52	56x	checkmate::assert_numeric(df[[ex_var]])
53	56x	checkmate::assert_true(all(.stats %in% c("n_patients", "sum_exposure")))
54
55	56x	row_label <- if (labelstr != "") {
56	!	labelstr
57	56x	} else if (!is.null(custom_label)) {
58	48x	custom_label
59		} else {
60	8x	"Total patients numbers/person time"
61		}
62
63	56x	y <- list()
64
65	56x	if ("n_patients" %in% .stats) {
66	23x	y$n_patients <-
67	23x	formatters::with_label(
68	23x	s_num_patients_content(
69	23x	df = df,
70	23x	.N_col = .N_col, # nolint
71	23x	.var = id,
72	23x	labelstr = ""
73	23x	)$unique,
74	23x	row_label
75		)
76		}
77	56x	if ("sum_exposure" %in% .stats) {
78	34x	y$sum_exposure <- formatters::with_label(sum(df[[ex_var]]), row_label)
79		}
80	56x	y
81		}
82
83		#' @describeIn summarize_patients_exposure_in_cols Analysis function which is used as `afun` in
84		#' [rtables::analyze_colvars()] within `analyze_patients_exposure_in_cols()` and as `cfun` in
85		#' [rtables::summarize_row_groups()] within `summarize_patients_exposure_in_cols()`.
86		#'
87		#' @return
88		#' * `a_count_patients_sum_exposure()` returns formatted [rtables::CellValue()].
89		#'
90		#' @examples
91		#' a_count_patients_sum_exposure(
92		#' df = df,
93		#' var = "SEX",
94		#' .N_col = nrow(df),
95		#' .stats = "n_patients"
96		#' )
97		#'
98		#' @export
99		a_count_patients_sum_exposure <- function(df,
100		var = NULL,
101		ex_var = "AVAL",
102		id = "USUBJID",
103		add_total_level = FALSE,
104		custom_label = NULL,
105		labelstr = "",
106		.N_col, # nolint
107		.stats,
108		.formats = list(n_patients = "xx (xx.x%)", sum_exposure = "xx")) {
109	32x	checkmate::assert_flag(add_total_level)
110
111	32x	if (!is.null(var)) {
112	21x	assert_df_with_variables(df, list(var = var))
113	21x	df[[var]] <- as.factor(df[[var]])
114		}
115
116	32x	y <- list()
117	32x	if (is.null(var)) {
118	11x	y[[.stats]] <- list(Total = s_count_patients_sum_exposure(
119	11x	df = df,
120	11x	ex_var = ex_var,
121	11x	id = id,
122	11x	labelstr = labelstr,
123	11x	.N_col = .N_col,
124	11x	.stats = .stats,
125	11x	custom_label = custom_label
126	11x	)[[.stats]])
127		} else {
128	21x	for (lvl in levels(df[[var]])) {
129	42x	y[[.stats]][[lvl]] <- s_count_patients_sum_exposure(
130	42x	df = subset(df, get(var) == lvl),
131	42x	ex_var = ex_var,
132	42x	id = id,
133	42x	labelstr = labelstr,
134	42x	.N_col = .N_col,
135	42x	.stats = .stats,
136	42x	custom_label = lvl
137	42x	)[[.stats]]
138		}
139	21x	if (add_total_level) {
140	2x	y[[.stats]][["Total"]] <- s_count_patients_sum_exposure(
141	2x	df = df,
142	2x	ex_var = ex_var,
143	2x	id = id,
144	2x	labelstr = labelstr,
145	2x	.N_col = .N_col,
146	2x	.stats = .stats,
147	2x	custom_label = custom_label
148	2x	)[[.stats]]
149		}
150		}
151
152	32x	in_rows(.list = y[[.stats]], .formats = .formats[[.stats]])
153		}
154
155		#' @describeIn summarize_patients_exposure_in_cols Layout-creating function which can take statistics
156		#' function arguments and additional format arguments. This function is a wrapper for
157		#' [rtables::split_cols_by_multivar()] and [rtables::summarize_row_groups()].
158		#'
159		#' @return
160		#' * `summarize_patients_exposure_in_cols()` returns a layout object suitable for passing to further
161		#' layouting functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will
162		#' add formatted content rows, with the statistics from `s_count_patients_sum_exposure()` arranged in
163		#' columns, to the table layout.
164		#'
165		#' @examples
166		#' lyt5 <- basic_table() %>%
167		#' summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE)
168		#'
169		#' result5 <- build_table(lyt5, df = df, alt_counts_df = adsl)
170		#' result5
171		#'
172		#' lyt6 <- basic_table() %>%
173		#' summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE, .stats = "sum_exposure")
174		#'
175		#' result6 <- build_table(lyt6, df = df, alt_counts_df = adsl)
176		#' result6
177		#'
178		#' @export
179		#' @order 3
180		summarize_patients_exposure_in_cols <- function(lyt, # nolint
181		var,
182		ex_var = "AVAL",
183		id = "USUBJID",
184		add_total_level = FALSE,
185		custom_label = NULL,
186		col_split = TRUE,
187		na_str = default_na_str(),
188		...,
189		.stats = c("n_patients", "sum_exposure"),
190		.labels = c(n_patients = "Patients", sum_exposure = "Person time"),
191		.indent_mods = NULL) {
192	3x	extra_args <- list(ex_var = ex_var, id = id, add_total_level = add_total_level, custom_label = custom_label, ...)
193
194	3x	if (col_split) {
195	3x	lyt <- split_cols_by_multivar(
196	3x	lyt = lyt,
197	3x	vars = rep(var, length(.stats)),
198	3x	varlabels = .labels[.stats],
199	3x	extra_args = list(.stats = .stats)
200		)
201		}
202	3x	summarize_row_groups(
203	3x	lyt = lyt,
204	3x	var = var,
205	3x	cfun = a_count_patients_sum_exposure,
206	3x	na_str = na_str,
207	3x	extra_args = extra_args
208		)
209		}
210
211		#' @describeIn summarize_patients_exposure_in_cols Layout-creating function which can take statistics
212		#' function arguments and additional format arguments. This function is a wrapper for
213		#' [rtables::split_cols_by_multivar()] and [rtables::analyze_colvars()].
214		#'
215		#' @param col_split (`flag`)\cr whether the columns should be split. Set to `FALSE` when the required
216		#' column split has been done already earlier in the layout pipe.
217		#'
218		#' @return
219		#' * `analyze_patients_exposure_in_cols()` returns a layout object suitable for passing to further
220		#' layouting functions, or to [rtables::build_table()]. Adding this function to an `rtable` layout will
221		#' add formatted data rows, with the statistics from `s_count_patients_sum_exposure()` arranged in
222		#' columns, to the table layout.
223		#'
224		#' @note As opposed to [summarize_patients_exposure_in_cols()] which generates content rows,
225		#' `analyze_patients_exposure_in_cols()` generates data rows which will _not_ be repeated on multiple
226		#' pages when pagination is used.
227		#'
228		#' @examples
229		#' set.seed(1)
230		#' df <- data.frame(
231		#' USUBJID = c(paste("id", seq(1, 12), sep = "")),
232		#' ARMCD = c(rep("ARM A", 6), rep("ARM B", 6)),
233		#' SEX = c(rep("Female", 6), rep("Male", 6)),
234		#' AVAL = as.numeric(sample(seq(1, 20), 12)),
235		#' stringsAsFactors = TRUE
236		#' )
237		#' adsl <- data.frame(
238		#' USUBJID = c(paste("id", seq(1, 12), sep = "")),
239		#' ARMCD = c(rep("ARM A", 2), rep("ARM B", 2)),
240		#' SEX = c(rep("Female", 2), rep("Male", 2)),
241		#' stringsAsFactors = TRUE
242		#' )
243		#'
244		#' lyt <- basic_table() %>%
245		#' split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
246		#' summarize_patients_exposure_in_cols(var = "AVAL", col_split = TRUE) %>%
247		#' analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE)
248		#' result <- build_table(lyt, df = df, alt_counts_df = adsl)
249		#' result
250		#'
251		#' lyt2 <- basic_table() %>%
252		#' split_cols_by("ARMCD", split_fun = add_overall_level("Total", first = FALSE)) %>%
253		#' summarize_patients_exposure_in_cols(
254		#' var = "AVAL", col_split = TRUE,
255		#' .stats = "n_patients", custom_label = "some custom label"
256		#' ) %>%
257		#' analyze_patients_exposure_in_cols(var = "SEX", col_split = FALSE, ex_var = "AVAL")
258		#' result2 <- build_table(lyt2, df = df, alt_counts_df = adsl)
259		#' result2
260		#'
261		#' lyt3 <- basic_table() %>%
262		#' analyze_patients_exposure_in_cols(var = "SEX", col_split = TRUE, ex_var = "AVAL")
263		#' result3 <- build_table(lyt3, df = df, alt_counts_df = adsl)
264		#' result3
265		#'
266		#' # Adding total levels and custom label
267		#' lyt4 <- basic_table(
268		#' show_colcounts = TRUE
269		#' ) %>%
270		#' analyze_patients_exposure_in_cols(
271		#' var = "ARMCD",
272		#' col_split = TRUE,
273		#' add_total_level = TRUE,
274		#' custom_label = "TOTAL"
275		#' ) %>%
276		#' append_topleft(c("", "Sex"))
277		#'
278		#' result4 <- build_table(lyt4, df = df, alt_counts_df = adsl)
279		#' result4
280		#'
281		#' @export
282		#' @order 2
283		analyze_patients_exposure_in_cols <- function(lyt, # nolint
284		var = NULL,
285		ex_var = "AVAL",
286		id = "USUBJID",
287		add_total_level = FALSE,
288		custom_label = NULL,
289		col_split = TRUE,
290		na_str = default_na_str(),
291		.stats = c("n_patients", "sum_exposure"),
292		.labels = c(n_patients = "Patients", sum_exposure = "Person time"),
293		.indent_mods = 0L,
294		...) {
295	6x	extra_args <- list(
296	6x	var = var, ex_var = ex_var, id = id, add_total_level = add_total_level, custom_label = custom_label, ...
297		)
298
299	6x	if (col_split) {
300	4x	lyt <- split_cols_by_multivar(
301	4x	lyt = lyt,
302	4x	vars = rep(ex_var, length(.stats)),
303	4x	varlabels = .labels[.stats],
304	4x	extra_args = list(.stats = .stats)
305		)
306		}
307	6x	lyt <- lyt %>% analyze_colvars(
308	6x	afun = a_count_patients_sum_exposure,
309	6x	indent_mod = .indent_mods,
310	6x	na_str = na_str,
311	6x	extra_args = extra_args
312		)
313	6x	lyt
314		}

1		#' Analyze numeric variables in columns
2		#'
3		#' @description `r lifecycle::badge("experimental")`
4		#'
5		#' The layout-creating function [analyze_vars_in_cols()] creates a layout element to generate a column-wise
6		#' analysis table.
7		#'
8		#' This function sets the analysis methods as column labels and is a wrapper for [rtables::analyze_colvars()].
9		#' It was designed principally for PK tables.
10		#'
11		#' @inheritParams argument_convention
12		#' @inheritParams rtables::analyze_colvars
13		#' @param imp_rule (`string` or `NULL`)\cr imputation rule setting. Defaults to `NULL` for no imputation rule. Can
14		#' also be `"1/3"` to implement 1/3 imputation rule or `"1/2"` to implement 1/2 imputation rule. In order
15		#' to use an imputation rule, the `avalcat_var` argument must be specified. See [imputation_rule()]
16		#' for more details on imputation.
17		#' @param avalcat_var (`string`)\cr if `imp_rule` is not `NULL`, name of variable that indicates whether a
18		#' row in the data corresponds to an analysis value in category `"BLQ"`, `"LTR"`, `"<PCLLOQ"`, or none of
19		#' the above (defaults to `"AVALCAT1"`). Variable must be present in the data and should match the variable
20		#' used to calculate the `n_blq` statistic (if included in `.stats`).
21		#' @param cache (`flag`)\cr whether to store computed values in a temporary caching environment. This will
22		#' speed up calculations in large tables, but should be set to `FALSE` if the same `rtable` layout is
23		#' used for multiple tables with different data. Defaults to `FALSE`.
24		#' @param row_labels (`character`)\cr as this function works in columns space, usually `.labels`
25		#' character vector applies on the column space. You can change the row labels by defining this
26		#' parameter to a named character vector with names corresponding to the split values. It defaults
27		#' to `NULL` and if it contains only one `string`, it will duplicate that as a row label.
28		#' @param do_summarize_row_groups (`flag`)\cr defaults to `FALSE` and applies the analysis to the current
29		#' label rows. This is a wrapper of [rtables::summarize_row_groups()] and it can accept `labelstr`
30		#' to define row labels. This behavior is not supported as we never need to overload row labels.
31		#' @param split_col_vars (`flag`)\cr defaults to `TRUE` and puts the analysis results onto the columns.
32		#' This option allows you to add multiple instances of this functions, also in a nested fashion,
33		#' without adding more splits. This split must happen only one time on a single layout.
34		#'
35		#' @return
36		#' A layout object suitable for passing to further layouting functions, or to [rtables::build_table()].
37		#' Adding this function to an `rtable` layout will summarize the given variables, arrange the output
38		#' in columns, and add it to the table layout.
39		#'
40		#' @note
41		#' * This is an experimental implementation of [rtables::summarize_row_groups()] and [rtables::analyze_colvars()]
42		#' that may be subjected to changes as `rtables` extends its support to more complex analysis pipelines in the
43		#' column space. We encourage users to read the examples carefully and file issues for different use cases.
44		#' * In this function, `labelstr` behaves atypically. If `labelstr = NULL` (the default), row labels are assigned
45		#' automatically as the split values if `do_summarize_row_groups = FALSE` (the default), and as the group label
46		#' if `do_summarize_row_groups = TRUE`.
47		#'
48		#' @seealso [analyze_vars()], [rtables::analyze_colvars()].
49		#'
50		#' @examples
51		#' library(dplyr)
52		#'
53		#' # Data preparation
54		#' adpp <- tern_ex_adpp %>% h_pkparam_sort()
55		#'
56		#' lyt <- basic_table() %>%
57		#' split_rows_by(var = "STRATA1", label_pos = "topleft") %>%
58		#' split_rows_by(
59		#' var = "SEX",
60		#' label_pos = "topleft",
61		#' child_labels = "hidden"
62		#' ) %>% # Removes duplicated labels
63		#' analyze_vars_in_cols(vars = "AGE")
64		#' result <- build_table(lyt = lyt, df = adpp)
65		#' result
66		#'
67		#' # By selecting just some statistics and ad-hoc labels
68		#' lyt <- basic_table() %>%
69		#' split_rows_by(var = "ARM", label_pos = "topleft") %>%
70		#' split_rows_by(
71		#' var = "SEX",
72		#' label_pos = "topleft",
73		#' child_labels = "hidden",
74		#' split_fun = drop_split_levels
75		#' ) %>%
76		#' analyze_vars_in_cols(
77		#' vars = "AGE",
78		#' .stats = c("n", "cv", "geom_mean"),
79		#' .labels = c(
80		#' n = "aN",
81		#' cv = "aCV",
82		#' geom_mean = "aGeomMean"
83		#' )
84		#' )
85		#' result <- build_table(lyt = lyt, df = adpp)
86		#' result
87		#'
88		#' # Changing row labels
89		#' lyt <- basic_table() %>%
90		#' analyze_vars_in_cols(
91		#' vars = "AGE",
92		#' row_labels = "some custom label"
93		#' )
94		#' result <- build_table(lyt, df = adpp)
95		#' result
96		#'
97		#' # Pharmacokinetic parameters
98		#' lyt <- basic_table() %>%
99		#' split_rows_by(
100		#' var = "TLG_DISPLAY",
101		#' split_label = "PK Parameter",
102		#' label_pos = "topleft",
103		#' child_labels = "hidden"
104		#' ) %>%
105		#' analyze_vars_in_cols(
106		#' vars = "AVAL"
107		#' )
108		#' result <- build_table(lyt, df = adpp)
109		#' result
110		#'
111		#' # Multiple calls (summarize label and analyze underneath)
112		#' lyt <- basic_table() %>%
113		#' split_rows_by(
114		#' var = "TLG_DISPLAY",
115		#' split_label = "PK Parameter",
116		#' label_pos = "topleft"
117		#' ) %>%
118		#' analyze_vars_in_cols(
119		#' vars = "AVAL",
120		#' do_summarize_row_groups = TRUE # does a summarize level
121		#' ) %>%
122		#' split_rows_by("SEX",
123		#' child_labels = "hidden",
124		#' label_pos = "topleft"
125		#' ) %>%
126		#' analyze_vars_in_cols(
127		#' vars = "AVAL",
128		#' split_col_vars = FALSE # avoids re-splitting the columns
129		#' )
130		#' result <- build_table(lyt, df = adpp)
131		#' result
132		#'
133		#' @export
134		analyze_vars_in_cols <- function(lyt,
135		vars,
136		...,
137		.stats = c(
138		"n",
139		"mean",
140		"sd",
141		"se",
142		"cv",
143		"geom_cv"
144		),
145		.labels = c(
146		n = "n",
147		mean = "Mean",
148		sd = "SD",
149		se = "SE",
150		cv = "CV (%)",
151		geom_cv = "CV % Geometric Mean"
152		),
153		row_labels = NULL,
154		do_summarize_row_groups = FALSE,
155		split_col_vars = TRUE,
156		imp_rule = NULL,
157		avalcat_var = "AVALCAT1",
158		cache = FALSE,
159		.indent_mods = NULL,
160		na_str = default_na_str(),
161		nested = TRUE,
162		.formats = NULL,
163		.aligns = NULL) {
164	26x	extra_args <- list(...)
165
166	26x	checkmate::assert_string(na_str, na.ok = TRUE, null.ok = TRUE)
167	26x	checkmate::assert_character(row_labels, null.ok = TRUE)
168	26x	checkmate::assert_int(.indent_mods, null.ok = TRUE)
169	26x	checkmate::assert_flag(nested)
170	26x	checkmate::assert_flag(split_col_vars)
171	26x	checkmate::assert_flag(do_summarize_row_groups)
172
173		# Filtering
174	26x	met_grps <- paste0("analyze_vars", c("_numeric", "_counts"))
175	26x	.stats <- get_stats(met_grps, stats_in = .stats)
176	26x	formats_v <- get_formats_from_stats(stats = .stats, formats_in = .formats)
177	26x	labels_v <- get_labels_from_stats(stats = .stats, labels_in = .labels) %>% .unlist_keep_nulls()
178	!	if ("control" %in% names(extra_args)) labels_v <- labels_v %>% labels_use_control(extra_args[["control"]], .labels)
179
180		# Check for vars in the case that one or more are used
181	26x	if (length(vars) == 1) {
182	21x	vars <- rep(vars, length(.stats))
183	5x	} else if (length(vars) != length(.stats)) {
184	1x	stop(
185	1x	"Analyzed variables (vars) does not have the same ",
186	1x	"number of elements of specified statistics (.stats)."
187		)
188		}
189
190	25x	if (split_col_vars) {
191		# Checking there is not a previous identical column split
192	21x	clyt <- tail(clayout(lyt), 1)[[1]]
193
194	21x	dummy_lyt <- split_cols_by_multivar(
195	21x	lyt = basic_table(),
196	21x	vars = vars,
197	21x	varlabels = labels_v
198		)
199
200	21x	if (any(sapply(clyt, identical, y = get_last_col_split(dummy_lyt)))) {
201	2x	stop(
202	2x	"Column split called again with the same values. ",
203	2x	"This can create many unwanted columns. Please consider adding ",
204	2x	"split_col_vars = FALSE to the last call of ",
205	2x	deparse(sys.calls()[[sys.nframe() - 1]]), "."
206		)
207		}
208
209		# Main col split
210	19x	lyt <- split_cols_by_multivar(
211	19x	lyt = lyt,
212	19x	vars = vars,
213	19x	varlabels = labels_v
214		)
215		}
216
217	23x	env <- new.env() # create caching environment
218
219	23x	if (do_summarize_row_groups) {
220	8x	if (length(unique(vars)) > 1) {
221	!	stop("When using do_summarize_row_groups only one label level var should be inserted.")
222		}
223
224		# Function list for do_summarize_row_groups. Slightly different handling of labels
225	8x	cfun_list <- Map(
226	8x	function(stat, use_cache, cache_env) {
227	48x	function(u, .spl_context, labelstr, .df_row, ...) {
228		# Statistic
229	152x	var_row_val <- paste(
230	152x	gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
231	152x	paste(.spl_context$value, collapse = "_"),
232	152x	sep = "_"
233		)
234	152x	if (use_cache) {
235	!	if (is.null(cache_env[[var_row_val]])) {
236	!	cache_env[[var_row_val]] <- s_summary(u, ...)
237		}
238	!	x_stats <- cache_env[[var_row_val]]
239		} else {
240	152x	x_stats <- s_summary(u, ...)
241		}
242
243	152x	if (is.null(imp_rule) \|\| !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
244	152x	res <- x_stats[[stat]]
245		} else {
246	!	timept <- as.numeric(gsub(".?([0-9\\.]+).", "\\1", tail(.spl_context$value, 1)))
247	!	res_imp <- imputation_rule(
248	!	.df_row, x_stats, stat,
249	!	imp_rule = imp_rule,
250	!	post = grepl("Predose", tail(.spl_context$value, 1)) \|\| timept > 0,
251	!	avalcat_var = avalcat_var
252		)
253	!	res <- res_imp[["val"]]
254	!	na_str <- res_imp[["na_str"]]
255		}
256
257		# Label check and replacement
258	152x	if (length(row_labels) > 1) {
259	32x	if (!(labelstr %in% names(row_labels))) {
260	2x	stop(
261	2x	"Replacing the labels in do_summarize_row_groups needs a named vector",
262	2x	"that contains the split values. In the current split variable ",
263	2x	.spl_context$split[nrow(.spl_context)],
264	2x	" the labelstr value (split value by default) ", labelstr, " is not in",
265	2x	" row_labels names: ", names(row_labels)
266		)
267		}
268	30x	lbl <- unlist(row_labels[labelstr])
269		} else {
270	120x	lbl <- labelstr
271		}
272
273		# Cell creation
274	150x	rcell(res,
275	150x	label = lbl,
276	150x	format = formats_v[names(formats_v) == stat][[1]],
277	150x	format_na_str = na_str,
278	150x	indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
279	150x	align = .aligns
280		)
281		}
282		},
283	8x	stat = .stats,
284	8x	use_cache = cache,
285	8x	cache_env = replicate(length(.stats), env)
286		)
287
288		# Main call to rtables
289	8x	summarize_row_groups(
290	8x	lyt = lyt,
291	8x	var = unique(vars),
292	8x	cfun = cfun_list,
293	8x	na_str = na_str,
294	8x	extra_args = extra_args
295		)
296		} else {
297		# Function list for analyze_colvars
298	15x	afun_list <- Map(
299	15x	function(stat, use_cache, cache_env) {
300	76x	function(u, .spl_context, .df_row, ...) {
301		# Main statistics
302	468x	var_row_val <- paste(
303	468x	gsub("\\._\\[\\[[0-9]+\\]\\]_\\.", "", paste(tail(.spl_context$cur_col_split_val, 1)[[1]], collapse = "_")),
304	468x	paste(.spl_context$value, collapse = "_"),
305	468x	sep = "_"
306		)
307	468x	if (use_cache) {
308	16x	if (is.null(cache_env[[var_row_val]])) cache_env[[var_row_val]] <- s_summary(u, ...)
309	56x	x_stats <- cache_env[[var_row_val]]
310		} else {
311	412x	x_stats <- s_summary(u, ...)
312		}
313
314	468x	if (is.null(imp_rule) \|\| !stat %in% c("mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max")) {
315	348x	res <- x_stats[[stat]]
316		} else {
317	120x	timept <- as.numeric(gsub(".?([0-9\\.]+).", "\\1", tail(.spl_context$value, 1)))
318	120x	res_imp <- imputation_rule(
319	120x	.df_row, x_stats, stat,
320	120x	imp_rule = imp_rule,
321	120x	post = grepl("Predose", tail(.spl_context$value, 1)) \|\| timept > 0,
322	120x	avalcat_var = avalcat_var
323		)
324	120x	res <- res_imp[["val"]]
325	120x	na_str <- res_imp[["na_str"]]
326		}
327
328	468x	if (is.list(res)) {
329	52x	if (length(res) > 1) {
330	1x	stop("The analyzed column produced more than one category of results.")
331		} else {
332	51x	res <- unlist(res)
333		}
334		}
335
336		# Label from context
337	467x	label_from_context <- .spl_context$value[nrow(.spl_context)]
338
339		# Label switcher
340	467x	if (is.null(row_labels)) {
341	387x	lbl <- label_from_context
342		} else {
343	80x	if (length(row_labels) > 1) {
344	68x	if (!(label_from_context %in% names(row_labels))) {
345	2x	stop(
346	2x	"Replacing the labels in do_summarize_row_groups needs a named vector",
347	2x	"that contains the split values. In the current split variable ",
348	2x	.spl_context$split[nrow(.spl_context)],
349	2x	" the split value ", label_from_context, " is not in",
350	2x	" row_labels names: ", names(row_labels)
351		)
352		}
353	66x	lbl <- unlist(row_labels[label_from_context])
354		} else {
355	12x	lbl <- row_labels
356		}
357		}
358
359		# Cell creation
360	465x	rcell(res,
361	465x	label = lbl,
362	465x	format = formats_v[names(formats_v) == stat][[1]],
363	465x	format_na_str = na_str,
364	465x	indent_mod = ifelse(is.null(.indent_mods), 0L, .indent_mods),
365	465x	align = .aligns
366		)
367		}
368		},
369	15x	stat = .stats,
370	15x	use_cache = cache,
371	15x	cache_env = replicate(length(.stats), env)
372		)
373
374		# Main call to rtables
375	15x	analyze_colvars(lyt,
376	15x	afun = afun_list,
377	15x	na_str = na_str,
378	15x	nested = nested,
379	15x	extra_args = extra_args
380		)
381		}
382		}
383
384		# Helper function
385		get_last_col_split <- function(lyt) {
386	3x	tail(tail(clayout(lyt), 1)[[1]], 1)[[1]]
387		}

1		#' Difference test for two proportions
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [test_proportion_diff()] creates a layout element to test the difference between two
6		#' proportions. The primary analysis variable, `vars`, indicates whether a response has occurred for each record. See
7		#' the `method` parameter for options of methods to use to calculate the p-value. Additionally, a stratification
8		#' variable can be supplied via the `strata` element of the `variables` argument.
9		#'
10		#' @inheritParams argument_convention
11		#' @param method (`string`)\cr one of `chisq`, `cmh`, `fisher`, or `schouten`; specifies the test used
12		#' to calculate the p-value.
13		#' @param .stats (`character`)\cr statistics to select for the table.
14		#'
15		#' Options are: ``r shQuote(get_stats("test_proportion_diff"), type = "sh")``
16		#'
17		#' @seealso [h_prop_diff_test]
18		#'
19		#' @name prop_diff_test
20		#' @order 1
21		NULL
22
23		#' @describeIn prop_diff_test Statistics function which tests the difference between two proportions.
24		#'
25		#' @return
26		#' * `s_test_proportion_diff()` returns a named `list` with a single item `pval` with an attribute `label`
27		#' describing the method used. The p-value tests the null hypothesis that proportions in two groups are the same.
28		#'
29		#' @keywords internal
30		s_test_proportion_diff <- function(df,
31		.var,
32		.ref_group,
33		.in_ref_col,
34		variables = list(strata = NULL),
35		method = c("chisq", "schouten", "fisher", "cmh")) {
36	45x	method <- match.arg(method)
37	45x	y <- list(pval = "")
38
39	45x	if (!.in_ref_col) {
40	45x	assert_df_with_variables(df, list(rsp = .var))
41	45x	assert_df_with_variables(.ref_group, list(rsp = .var))
42	45x	rsp <- factor(
43	45x	c(.ref_group[[.var]], df[[.var]]),
44	45x	levels = c("TRUE", "FALSE")
45		)
46	45x	grp <- factor(
47	45x	rep(c("ref", "Not-ref"), c(nrow(.ref_group), nrow(df))),
48	45x	levels = c("ref", "Not-ref")
49		)
50
51	45x	if (!is.null(variables$strata) \|\| method == "cmh") {
52	12x	strata <- variables$strata
53	12x	checkmate::assert_false(is.null(strata))
54	12x	strata_vars <- stats::setNames(as.list(strata), strata)
55	12x	assert_df_with_variables(df, strata_vars)
56	12x	assert_df_with_variables(.ref_group, strata_vars)
57	12x	strata <- c(interaction(.ref_group[strata]), interaction(df[strata]))
58		}
59
60	45x	tbl <- switch(method,
61	45x	cmh = table(grp, rsp, strata),
62	45x	table(grp, rsp)
63		)
64
65	45x	y$pval <- switch(method,
66	45x	chisq = prop_chisq(tbl),
67	45x	cmh = prop_cmh(tbl),
68	45x	fisher = prop_fisher(tbl),
69	45x	schouten = prop_schouten(tbl)
70		)
71		}
72
73	45x	y$pval <- formatters::with_label(y$pval, d_test_proportion_diff(method))
74	45x	y
75		}
76
77		#' Description of the difference test between two proportions
78		#'
79		#' @description `r lifecycle::badge("stable")`
80		#'
81		#' This is an auxiliary function that describes the analysis in `s_test_proportion_diff`.
82		#'
83		#' @inheritParams s_test_proportion_diff
84		#'
85		#' @return A `string` describing the test from which the p-value is derived.
86		#'
87		#' @export
88		d_test_proportion_diff <- function(method) {
89	59x	checkmate::assert_string(method)
90	59x	meth_part <- switch(method,
91	59x	"schouten" = "Chi-Squared Test with Schouten Correction",
92	59x	"chisq" = "Chi-Squared Test",
93	59x	"cmh" = "Cochran-Mantel-Haenszel Test",
94	59x	"fisher" = "Fisher's Exact Test",
95	59x	stop(paste(method, "does not have a description"))
96		)
97	59x	paste0("p-value (", meth_part, ")")
98		}
99
100		#' @describeIn prop_diff_test Formatted analysis function which is used as `afun` in `test_proportion_diff()`.
101		#'
102		#' @return
103		#' * `a_test_proportion_diff()` returns the corresponding list with formatted [rtables::CellValue()].
104		#'
105		#' @keywords internal
106		a_test_proportion_diff <- make_afun(
107		s_test_proportion_diff,
108		.formats = c(pval = "x.xxxx \| (<0.0001)"),
109		.indent_mods = c(pval = 1L)
110		)
111
112		#' @describeIn prop_diff_test Layout-creating function which can take statistics function arguments
113		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
114		#'
115		#' @return
116		#' * `test_proportion_diff()` returns a layout object suitable for passing to further layouting functions,
117		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
118		#' the statistics from `s_test_proportion_diff()` to the table layout.
119		#'
120		#' @examples
121		#' dta <- data.frame(
122		#' rsp = sample(c(TRUE, FALSE), 100, TRUE),
123		#' grp = factor(rep(c("A", "B"), each = 50)),
124		#' strata = factor(rep(c("V", "W", "X", "Y", "Z"), each = 20))
125		#' )
126		#'
127		#' # With `rtables` pipelines.
128		#' l <- basic_table() %>%
129		#' split_cols_by(var = "grp", ref_group = "B") %>%
130		#' test_proportion_diff(
131		#' vars = "rsp",
132		#' method = "cmh", variables = list(strata = "strata")
133		#' )
134		#'
135		#' build_table(l, df = dta)
136		#'
137		#' @export
138		#' @order 2
139		test_proportion_diff <- function(lyt,
140		vars,
141		variables = list(strata = NULL),
142		method = c("chisq", "schouten", "fisher", "cmh"),
143		na_str = default_na_str(),
144		nested = TRUE,
145		...,
146		var_labels = vars,
147		show_labels = "hidden",
148		table_names = vars,
149		.stats = NULL,
150		.formats = NULL,
151		.labels = NULL,
152		.indent_mods = NULL) {
153	6x	extra_args <- list(variables = variables, method = method, ...)
154
155	6x	afun <- make_afun(
156	6x	a_test_proportion_diff,
157	6x	.stats = .stats,
158	6x	.formats = .formats,
159	6x	.labels = .labels,
160	6x	.indent_mods = .indent_mods
161		)
162	6x	analyze(
163	6x	lyt,
164	6x	vars,
165	6x	afun = afun,
166	6x	var_labels = var_labels,
167	6x	na_str = na_str,
168	6x	nested = nested,
169	6x	extra_args = extra_args,
170	6x	show_labels = show_labels,
171	6x	table_names = table_names
172		)
173		}
174
175		#' Helper functions to test proportion differences
176		#'
177		#' Helper functions to implement various tests on the difference between two proportions.
178		#'
179		#' @param tbl (`matrix`)\cr matrix with two groups in rows and the binary response (`TRUE`/`FALSE`) in columns.
180		#'
181		#' @return A p-value.
182		#'
183		#' @seealso [prop_diff_test()] for implementation of these helper functions.
184		#'
185		#' @name h_prop_diff_test
186		NULL
187
188		#' @describeIn h_prop_diff_test Performs Chi-Squared test. Internally calls [stats::prop.test()].
189		#'
190		#' @keywords internal
191		prop_chisq <- function(tbl) {
192	41x	checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
193	41x	tbl <- tbl[, c("TRUE", "FALSE")]
194	41x	if (any(colSums(tbl) == 0)) {
195	2x	return(1)
196		}
197	39x	stats::prop.test(tbl, correct = FALSE)$p.value
198		}
199
200		#' @describeIn h_prop_diff_test Performs stratified Cochran-Mantel-Haenszel test. Internally calls
201		#' [stats::mantelhaen.test()]. Note that strata with less than two observations are automatically discarded.
202		#'
203		#' @param ary (`array`, 3 dimensions)\cr array with two groups in rows, the binary response
204		#' (`TRUE`/`FALSE`) in columns, and the strata in the third dimension.
205		#'
206		#' @keywords internal
207		prop_cmh <- function(ary) {
208	16x	checkmate::assert_array(ary)
209	16x	checkmate::assert_integer(c(ncol(ary), nrow(ary)), lower = 2, upper = 2)
210	16x	checkmate::assert_integer(length(dim(ary)), lower = 3, upper = 3)
211	16x	strata_sizes <- apply(ary, MARGIN = 3, sum)
212	16x	if (any(strata_sizes < 5)) {
213	1x	warning("<5 data points in some strata. CMH test may be incorrect.")
214	1x	ary <- ary[, , strata_sizes > 1]
215		}
216
217	16x	stats::mantelhaen.test(ary, correct = FALSE)$p.value
218		}
219
220		#' @describeIn h_prop_diff_test Performs the Chi-Squared test with Schouten correction.
221		#'
222		#' @seealso Schouten correction is based upon \insertCite{Schouten1980-kd;textual}{tern}.
223		#'
224		#' @keywords internal
225		prop_schouten <- function(tbl) {
226	100x	checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
227	100x	tbl <- tbl[, c("TRUE", "FALSE")]
228	100x	if (any(colSums(tbl) == 0)) {
229	1x	return(1)
230		}
231
232	99x	n <- sum(tbl)
233	99x	n1 <- sum(tbl[1, ])
234	99x	n2 <- sum(tbl[2, ])
235
236	99x	ad <- diag(tbl)
237	99x	bc <- diag(apply(tbl, 2, rev))
238	99x	ac <- tbl[, 1]
239	99x	bd <- tbl[, 2]
240
241	99x	t_schouten <- (n - 1) *
242	99x	(abs(prod(ad) - prod(bc)) - 0.5 * min(n1, n2))^2 /
243	99x	(n1 * n2 * sum(ac) * sum(bd))
244
245	99x	1 - stats::pchisq(t_schouten, df = 1)
246		}
247
248		#' @describeIn h_prop_diff_test Performs the Fisher's exact test. Internally calls [stats::fisher.test()].
249		#'
250		#' @keywords internal
251		prop_fisher <- function(tbl) {
252	2x	checkmate::assert_integer(c(ncol(tbl), nrow(tbl)), lower = 2, upper = 2)
253	2x	tbl <- tbl[, c("TRUE", "FALSE")]
254	2x	stats::fisher.test(tbl)$p.value
255		}

1		#' Stack multiple grobs
2		#'
3		#' @description `r lifecycle::badge("deprecated")`
4		#'
5		#' Stack grobs as a new grob with 1 column and multiple rows layout.
6		#'
7		#' @param ... grobs.
8		#' @param grobs (`list` of `grob`)\cr a list of grobs.
9		#' @param padding (`grid::unit`)\cr unit of length 1, space between each grob.
10		#' @param vp (`viewport` or `NULL`)\cr a [viewport()] object (or `NULL`).
11		#' @param name (`string`)\cr a character identifier for the grob.
12		#' @param gp (`gpar`)\cr a [gpar()] object.
13		#'
14		#' @return A `grob`.
15		#'
16		#' @examples
17		#' library(grid)
18		#'
19		#' g1 <- circleGrob(gp = gpar(col = "blue"))
20		#' g2 <- circleGrob(gp = gpar(col = "red"))
21		#' g3 <- textGrob("TEST TEXT")
22		#' grid.newpage()
23		#' grid.draw(stack_grobs(g1, g2, g3))
24		#'
25		#' showViewport()
26		#'
27		#' grid.newpage()
28		#' pushViewport(viewport(layout = grid.layout(1, 2)))
29		#' vp1 <- viewport(layout.pos.row = 1, layout.pos.col = 2)
30		#' grid.draw(stack_grobs(g1, g2, g3, vp = vp1, name = "test"))
31		#'
32		#' showViewport()
33		#' grid.ls(grobs = TRUE, viewports = TRUE, print = FALSE)
34		#'
35		#' @export
36		stack_grobs <- function(...,
37		grobs = list(...),
38		padding = grid::unit(2, "line"),
39		vp = NULL,
40		gp = NULL,
41		name = NULL) {
42	4x	lifecycle::deprecate_warn(
43	4x	"0.9.4",
44	4x	"stack_grobs()",
45	4x	details = "`tern` plotting functions no longer generate `grob` objects."
46		)
47
48	4x	checkmate::assert_true(
49	4x	all(vapply(grobs, grid::is.grob, logical(1)))
50		)
51
52	4x	if (length(grobs) == 1) {
53	1x	return(grobs[[1]])
54		}
55
56	3x	n_layout <- 2 * length(grobs) - 1
57	3x	hts <- lapply(
58	3x	seq(1, n_layout),
59	3x	function(i) {
60	39x	if (i %% 2 != 0) {
61	21x	grid::unit(1, "null")
62		} else {
63	18x	padding
64		}
65		}
66		)
67	3x	hts <- do.call(grid::unit.c, hts)
68
69	3x	main_vp <- grid::viewport(
70	3x	layout = grid::grid.layout(nrow = n_layout, ncol = 1, heights = hts)
71		)
72
73	3x	nested_grobs <- Map(function(g, i) {
74	21x	grid::gTree(
75	21x	children = grid::gList(g),
76	21x	vp = grid::viewport(layout.pos.row = i, layout.pos.col = 1)
77		)
78	3x	}, grobs, seq_along(grobs) * 2 - 1)
79
80	3x	grobs_mainvp <- grid::gTree(
81	3x	children = do.call(grid::gList, nested_grobs),
82	3x	vp = main_vp
83		)
84
85	3x	grid::gTree(
86	3x	children = grid::gList(grobs_mainvp),
87	3x	vp = vp,
88	3x	gp = gp,
89	3x	name = name
90		)
91		}
92
93		#' Arrange multiple grobs
94		#'
95		#' @description `r lifecycle::badge("deprecated")`
96		#'
97		#' Arrange grobs as a new grob with `n * m (rows * cols)` layout.
98		#'
99		#' @inheritParams stack_grobs
100		#' @param ncol (`integer(1)`)\cr number of columns in layout.
101		#' @param nrow (`integer(1)`)\cr number of rows in layout.
102		#' @param padding_ht (`grid::unit`)\cr unit of length 1, vertical space between each grob.
103		#' @param padding_wt (`grid::unit`)\cr unit of length 1, horizontal space between each grob.
104		#'
105		#' @return A `grob`.
106		#'
107		#' @examples
108		#' library(grid)
109		#'
110		#' \donttest{
111		#' num <- lapply(1:9, textGrob)
112		#' grid::grid.newpage()
113		#' grid.draw(arrange_grobs(grobs = num, ncol = 2))
114		#'
115		#' showViewport()
116		#'
117		#' g1 <- circleGrob(gp = gpar(col = "blue"))
118		#' g2 <- circleGrob(gp = gpar(col = "red"))
119		#' g3 <- textGrob("TEST TEXT")
120		#' grid::grid.newpage()
121		#' grid.draw(arrange_grobs(g1, g2, g3, nrow = 2))
122		#'
123		#' showViewport()
124		#'
125		#' grid::grid.newpage()
126		#' grid.draw(arrange_grobs(g1, g2, g3, ncol = 3))
127		#'
128		#' grid::grid.newpage()
129		#' grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2)))
130		#' vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2)
131		#' grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1))
132		#'
133		#' showViewport()
134		#' }
135		#' @export
136		arrange_grobs <- function(...,
137		grobs = list(...),
138		ncol = NULL, nrow = NULL,
139		padding_ht = grid::unit(2, "line"),
140		padding_wt = grid::unit(2, "line"),
141		vp = NULL,
142		gp = NULL,
143		name = NULL) {
144	5x	lifecycle::deprecate_warn(
145	5x	"0.9.4",
146	5x	"arrange_grobs()",
147	5x	details = "`tern` plotting functions no longer generate `grob` objects."
148		)
149
150	5x	checkmate::assert_true(
151	5x	all(vapply(grobs, grid::is.grob, logical(1)))
152		)
153
154	5x	if (length(grobs) == 1) {
155	1x	return(grobs[[1]])
156		}
157
158	4x	if (is.null(ncol) && is.null(nrow)) {
159	1x	ncol <- 1
160	1x	nrow <- ceiling(length(grobs) / ncol)
161	3x	} else if (!is.null(ncol) && is.null(nrow)) {
162	1x	nrow <- ceiling(length(grobs) / ncol)
163	2x	} else if (is.null(ncol) && !is.null(nrow)) {
164	!	ncol <- ceiling(length(grobs) / nrow)
165		}
166
167	4x	if (ncol * nrow < length(grobs)) {
168	1x	stop("specififed ncol and nrow are not enough for arranging the grobs ")
169		}
170
171	3x	if (ncol == 1) {
172	2x	return(stack_grobs(grobs = grobs, padding = padding_ht, vp = vp, gp = gp, name = name))
173		}
174
175	1x	n_col <- 2 * ncol - 1
176	1x	n_row <- 2 * nrow - 1
177	1x	hts <- lapply(
178	1x	seq(1, n_row),
179	1x	function(i) {
180	5x	if (i %% 2 != 0) {
181	3x	grid::unit(1, "null")
182		} else {
183	2x	padding_ht
184		}
185		}
186		)
187	1x	hts <- do.call(grid::unit.c, hts)
188
189	1x	wts <- lapply(
190	1x	seq(1, n_col),
191	1x	function(i) {
192	5x	if (i %% 2 != 0) {
193	3x	grid::unit(1, "null")
194		} else {
195	2x	padding_wt
196		}
197		}
198		)
199	1x	wts <- do.call(grid::unit.c, wts)
200
201	1x	main_vp <- grid::viewport(
202	1x	layout = grid::grid.layout(nrow = n_row, ncol = n_col, widths = wts, heights = hts)
203		)
204
205	1x	nested_grobs <- list()
206	1x	k <- 0
207	1x	for (i in seq(nrow) * 2 - 1) {
208	3x	for (j in seq(ncol) * 2 - 1) {
209	9x	k <- k + 1
210	9x	if (k <= length(grobs)) {
211	9x	nested_grobs <- c(
212	9x	nested_grobs,
213	9x	list(grid::gTree(
214	9x	children = grid::gList(grobs[[k]]),
215	9x	vp = grid::viewport(layout.pos.row = i, layout.pos.col = j)
216		))
217		)
218		}
219		}
220		}
221	1x	grobs_mainvp <- grid::gTree(
222	1x	children = do.call(grid::gList, nested_grobs),
223	1x	vp = main_vp
224		)
225
226	1x	grid::gTree(
227	1x	children = grid::gList(grobs_mainvp),
228	1x	vp = vp,
229	1x	gp = gp,
230	1x	name = name
231		)
232		}
233
234		#' Draw `grob`
235		#'
236		#' @description `r lifecycle::badge("deprecated")`
237		#'
238		#' Draw grob on device page.
239		#'
240		#' @param grob (`grob`)\cr grid object.
241		#' @param newpage (`flag`)\cr draw on a new page.
242		#' @param vp (`viewport` or `NULL`)\cr a [viewport()] object (or `NULL`).
243		#'
244		#' @return A `grob`.
245		#'
246		#' @examples
247		#' library(dplyr)
248		#' library(grid)
249		#'
250		#' \donttest{
251		#' rect <- rectGrob(width = grid::unit(0.5, "npc"), height = grid::unit(0.5, "npc"))
252		#' rect %>% draw_grob(vp = grid::viewport(angle = 45))
253		#'
254		#' num <- lapply(1:10, textGrob)
255		#' num %>%
256		#' arrange_grobs(grobs = .) %>%
257		#' draw_grob()
258		#' showViewport()
259		#' }
260		#'
261		#' @export
262		draw_grob <- function(grob, newpage = TRUE, vp = NULL) {
263	3x	lifecycle::deprecate_warn(
264	3x	"0.9.4",
265	3x	"draw_grob()",
266	3x	details = "`tern` plotting functions no longer generate `grob` objects."
267		)
268
269	3x	if (newpage) {
270	3x	grid::grid.newpage()
271		}
272	3x	if (!is.null(vp)) {
273	1x	grid::pushViewport(vp)
274		}
275	3x	grid::grid.draw(grob)
276		}
277
278		tern_grob <- function(x) {
279	!	class(x) <- unique(c("ternGrob", class(x)))
280	!	x
281		}
282
283		#' @keywords internal
284		print.ternGrob <- function(x, ...) {
285	!	grid::grid.newpage()
286	!	grid::grid.draw(x)
287		}

1		#' Tabulate binary response by subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The [tabulate_rsp_subgroups()] function creates a layout element to tabulate binary response by subgroup, returning
6		#' statistics including response rate and odds ratio for each population subgroup. The table is created from `df`, a
7		#' list of data frames returned by [extract_rsp_subgroups()], with the statistics to include specified via the `vars`
8		#' parameter.
9		#'
10		#' A forest plot can be created from the resulting table using the [g_forest()] function.
11		#'
12		#' @inheritParams extract_rsp_subgroups
13		#' @inheritParams argument_convention
14		#'
15		#' @details These functions create a layout starting from a data frame which contains
16		#' the required statistics. Tables typically used as part of forest plot.
17		#'
18		#' @seealso [extract_rsp_subgroups()]
19		#'
20		#' @examples
21		#' library(dplyr)
22		#' library(forcats)
23		#'
24		#' adrs <- tern_ex_adrs
25		#' adrs_labels <- formatters::var_labels(adrs)
26		#'
27		#' adrs_f <- adrs %>%
28		#' filter(PARAMCD == "BESRSPI") %>%
29		#' filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
30		#' droplevels() %>%
31		#' mutate(
32		#' # Reorder levels of factor to make the placebo group the reference arm.
33		#' ARM = fct_relevel(ARM, "B: Placebo"),
34		#' rsp = AVALC == "CR"
35		#' )
36		#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
37		#'
38		#' # Unstratified analysis.
39		#' df <- extract_rsp_subgroups(
40		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
41		#' data = adrs_f
42		#' )
43		#' df
44		#'
45		#' # Stratified analysis.
46		#' df_strat <- extract_rsp_subgroups(
47		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2"), strata = "STRATA1"),
48		#' data = adrs_f
49		#' )
50		#' df_strat
51		#'
52		#' # Grouping of the BMRKR2 levels.
53		#' df_grouped <- extract_rsp_subgroups(
54		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
55		#' data = adrs_f,
56		#' groups_lists = list(
57		#' BMRKR2 = list(
58		#' "low" = "LOW",
59		#' "low/medium" = c("LOW", "MEDIUM"),
60		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
61		#' )
62		#' )
63		#' )
64		#' df_grouped
65		#'
66		#' @name response_subgroups
67		#' @order 1
68		NULL
69
70		#' Prepare response data for population subgroups in data frames
71		#'
72		#' @description `r lifecycle::badge("stable")`
73		#'
74		#' Prepares response rates and odds ratios for population subgroups in data frames. Simple wrapper
75		#' for [h_odds_ratio_subgroups_df()] and [h_proportion_subgroups_df()]. Result is a list of two
76		#' `data.frames`: `prop` and `or`. `variables` corresponds to the names of variables found in `data`,
77		#' passed as a named `list` and requires elements `rsp`, `arm` and optionally `subgroups` and `strata`.
78		#' `groups_lists` optionally specifies groupings for `subgroups` variables.
79		#'
80		#' @inheritParams argument_convention
81		#' @inheritParams response_subgroups
82		#' @param label_all (`string`)\cr label for the total population analysis.
83		#'
84		#' @return A named list of two elements:
85		#' * `prop`: A `data.frame` containing columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`, `var`,
86		#' `var_label`, and `row_type`.
87		#' * `or`: A `data.frame` containing columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`,
88		#' `subgroup`, `var`, `var_label`, and `row_type`.
89		#'
90		#' @seealso [response_subgroups]
91		#'
92		#' @export
93		extract_rsp_subgroups <- function(variables,
94		data,
95		groups_lists = list(),
96		conf_level = 0.95,
97		method = NULL,
98		label_all = "All Patients") {
99	14x	if ("strat" %in% names(variables)) {
100	!	warning(
101	!	"Warning: the `strat` element name of the `variables` list argument to `extract_rsp_subgroups() ",
102	!	"was deprecated in tern 0.9.4.\n ",
103	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
104		)
105	!	variables[["strata"]] <- variables[["strat"]]
106		}
107
108	14x	df_prop <- h_proportion_subgroups_df(
109	14x	variables,
110	14x	data,
111	14x	groups_lists = groups_lists,
112	14x	label_all = label_all
113		)
114	14x	df_or <- h_odds_ratio_subgroups_df(
115	14x	variables,
116	14x	data,
117	14x	groups_lists = groups_lists,
118	14x	conf_level = conf_level,
119	14x	method = method,
120	14x	label_all = label_all
121		)
122
123	14x	list(prop = df_prop, or = df_or)
124		}
125
126		#' @describeIn response_subgroups Formatted analysis function which is used as `afun` in `tabulate_rsp_subgroups()`.
127		#'
128		#' @return
129		#' * `a_response_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
130		#'
131		#' @keywords internal
132		a_response_subgroups <- function(.formats = list(
133		n = "xx", # nolint start
134		n_rsp = "xx",
135		prop = "xx.x%",
136		n_tot = "xx",
137		or = list(format_extreme_values(2L)),
138		ci = list(format_extreme_values_ci(2L)),
139		pval = "x.xxxx \| (<0.0001)",
140		riskdiff = "xx.x (xx.x - xx.x)" # nolint end
141		),
142		na_str = default_na_str()) {
143	22x	checkmate::assert_list(.formats)
144	22x	checkmate::assert_subset(
145	22x	names(.formats),
146	22x	c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval", "riskdiff")
147		)
148
149	22x	afun_lst <- Map(
150	22x	function(stat, fmt, na_str) {
151	157x	function(df, labelstr = "", ...) {
152	349x	in_rows(
153	349x	.list = as.list(df[[stat]]),
154	349x	.labels = as.character(df$subgroup),
155	349x	.formats = fmt,
156	349x	.format_na_strs = na_str
157		)
158		}
159		},
160	22x	stat = names(.formats),
161	22x	fmt = .formats,
162	22x	na_str = na_str
163		)
164
165	22x	afun_lst
166		}
167
168		#' @describeIn response_subgroups Table-creating function which creates a table
169		#' summarizing binary response by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
170		#' and [rtables::summarize_row_groups()].
171		#'
172		#' @param df (`list`)\cr a list of data frames containing all analysis variables. List should be
173		#' created using [extract_rsp_subgroups()].
174		#' @param vars (`character`)\cr the names of statistics to be reported among:
175		#' * `n`: Total number of observations per group.
176		#' * `n_rsp`: Number of responders per group.
177		#' * `prop`: Proportion of responders.
178		#' * `n_tot`: Total number of observations.
179		#' * `or`: Odds ratio.
180		#' * `ci` : Confidence interval of odds ratio.
181		#' * `pval`: p-value of the effect.
182		#' Note, the statistics `n_tot`, `or`, and `ci` are required.
183		#' @param riskdiff (`list`)\cr if a risk (proportion) difference column should be added, a list of settings to apply
184		#' within the column. See [control_riskdiff()] for details. If `NULL`, no risk difference column will be added. If
185		#' `riskdiff$arm_x` and `riskdiff$arm_y` are `NULL`, the first level of `df$prop$arm` will be used as `arm_x` and
186		#' the second level as `arm_y`.
187		#'
188		#' @return An `rtables` table summarizing binary response by subgroup.
189		#'
190		#' @examples
191		#' # Table with default columns
192		#' basic_table() %>%
193		#' tabulate_rsp_subgroups(df)
194		#'
195		#' # Table with selected columns
196		#' basic_table() %>%
197		#' tabulate_rsp_subgroups(
198		#' df = df,
199		#' vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci")
200		#' )
201		#'
202		#' # Table with risk difference column added
203		#' basic_table() %>%
204		#' tabulate_rsp_subgroups(
205		#' df,
206		#' riskdiff = control_riskdiff(
207		#' arm_x = levels(df$prop$arm)[1],
208		#' arm_y = levels(df$prop$arm)[2]
209		#' )
210		#' )
211		#'
212		#' @export
213		#' @order 2
214		tabulate_rsp_subgroups <- function(lyt,
215		df,
216		vars = c("n_tot", "n", "prop", "or", "ci"),
217		groups_lists = list(),
218		label_all = "All Patients",
219		riskdiff = NULL,
220		na_str = default_na_str(),
221		.formats = c(
222		n = "xx", n_rsp = "xx", prop = "xx.x%", n_tot = "xx",
223		or = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)),
224		pval = "x.xxxx \| (<0.0001)"
225		)) {
226	13x	checkmate::assert_list(riskdiff, null.ok = TRUE)
227	13x	checkmate::assert_true(all(c("n_tot", "or", "ci") %in% vars))
228	13x	if ("pval" %in% vars && !"pval" %in% names(df$or)) {
229	1x	warning(
230	1x	'The "pval" statistic has been selected but is not present in "df" so it will not be included in the output ',
231	1x	'table. To include the "pval" statistic, please specify a p-value test when generating "df" via ',
232	1x	'the "method" argument to `extract_rsp_subgroups()`. If method = "cmh", strata must also be specified via the ',
233	1x	'"variables" argument to `extract_rsp_subgroups()`.'
234		)
235		}
236
237		# Create "ci" column from "lcl" and "ucl"
238	13x	df$or$ci <- combine_vectors(df$or$lcl, df$or$ucl)
239
240		# Fill in missing formats with defaults
241	13x	default_fmts <- eval(formals(tabulate_rsp_subgroups)$.formats)
242	13x	.formats <- c(.formats, default_fmts[vars[!vars %in% names(.formats)]])
243
244		# Extract additional parameters from df
245	13x	conf_level <- df$or$conf_level[1]
246	13x	method <- if ("pval_label" %in% names(df$or)) df$or$pval_label[1] else NULL
247	13x	colvars <- d_rsp_subgroups_colvars(vars, conf_level = conf_level, method = method)
248	13x	prop_vars <- intersect(colvars$vars, c("n", "prop", "n_rsp"))
249	13x	or_vars <- intersect(names(colvars$labels), c("n_tot", "or", "ci", "pval"))
250	13x	colvars_prop <- list(vars = prop_vars, labels = colvars$labels[prop_vars])
251	13x	colvars_or <- list(vars = or_vars, labels = colvars$labels[or_vars])
252
253	13x	extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method, label_all = label_all)
254
255		# Get analysis function for each statistic
256	13x	afun_lst <- a_response_subgroups(.formats = c(.formats, riskdiff = riskdiff$format), na_str = na_str)
257
258		# Add risk difference column
259	13x	if (!is.null(riskdiff)) {
260	!	if (is.null(riskdiff$arm_x)) riskdiff$arm_x <- levels(df$prop$arm)[1]
261	!	if (is.null(riskdiff$arm_y)) riskdiff$arm_y <- levels(df$prop$arm)[2]
262	1x	colvars_or$vars <- c(colvars_or$vars, "riskdiff")
263	1x	colvars_or$labels <- c(colvars_or$labels, riskdiff = riskdiff$col_label)
264	1x	arm_cols <- paste(rep(c("n_rsp", "n_rsp", "n", "n")), c(riskdiff$arm_x, riskdiff$arm_y), sep = "_")
265
266	1x	df_prop_diff <- df$prop %>%
267	1x	dplyr::select(-"prop") %>%
268	1x	tidyr::pivot_wider(
269	1x	id_cols = c("subgroup", "var", "var_label", "row_type"),
270	1x	names_from = "arm",
271	1x	values_from = c("n", "n_rsp")
272		) %>%
273	1x	dplyr::rowwise() %>%
274	1x	dplyr::mutate(
275	1x	riskdiff = stat_propdiff_ci(
276	1x	x = as.list(.data[[arm_cols[1]]]),
277	1x	y = as.list(.data[[arm_cols[2]]]),
278	1x	N_x = .data[[arm_cols[3]]],
279	1x	N_y = .data[[arm_cols[4]]]
280		)
281		) %>%
282	1x	dplyr::select(-dplyr::all_of(arm_cols))
283
284	1x	df$or <- df$or %>%
285	1x	dplyr::left_join(
286	1x	df_prop_diff,
287	1x	by = c("subgroup", "var", "var_label", "row_type")
288		)
289		}
290
291		# Add columns from table_prop (optional)
292	13x	if (length(colvars_prop$vars) > 0) {
293	12x	lyt_prop <- split_cols_by(lyt = lyt, var = "arm")
294	12x	lyt_prop <- split_cols_by_multivar(
295	12x	lyt = lyt_prop,
296	12x	vars = colvars_prop$vars,
297	12x	varlabels = colvars_prop$labels
298		)
299
300		# Add "All Patients" row
301	12x	lyt_prop <- split_rows_by(
302	12x	lyt = lyt_prop,
303	12x	var = "row_type",
304	12x	split_fun = keep_split_levels("content"),
305	12x	nested = FALSE,
306	12x	child_labels = "hidden"
307		)
308	12x	lyt_prop <- analyze_colvars(
309	12x	lyt = lyt_prop,
310	12x	afun = afun_lst[names(colvars_prop$labels)],
311	12x	na_str = na_str,
312	12x	extra_args = extra_args
313		)
314
315		# Add analysis rows
316	12x	if ("analysis" %in% df$prop$row_type) {
317	11x	lyt_prop <- split_rows_by(
318	11x	lyt = lyt_prop,
319	11x	var = "row_type",
320	11x	split_fun = keep_split_levels("analysis"),
321	11x	nested = FALSE,
322	11x	child_labels = "hidden"
323		)
324	11x	lyt_prop <- split_rows_by(lyt = lyt_prop, var = "var_label", nested = TRUE)
325	11x	lyt_prop <- analyze_colvars(
326	11x	lyt = lyt_prop,
327	11x	afun = afun_lst[names(colvars_prop$labels)],
328	11x	na_str = na_str,
329	11x	inclNAs = TRUE,
330	11x	extra_args = extra_args
331		)
332		}
333
334	12x	table_prop <- build_table(lyt_prop, df = df$prop)
335		} else {
336	1x	table_prop <- NULL
337		}
338
339		# Add columns from table_or ("n_tot", "or", and "ci" required)
340	13x	lyt_or <- split_cols_by(lyt = lyt, var = "arm")
341	13x	lyt_or <- split_cols_by_multivar(
342	13x	lyt = lyt_or,
343	13x	vars = colvars_or$vars,
344	13x	varlabels = colvars_or$labels
345		)
346
347		# Add "All Patients" row
348	13x	lyt_or <- split_rows_by(
349	13x	lyt = lyt_or,
350	13x	var = "row_type",
351	13x	split_fun = keep_split_levels("content"),
352	13x	nested = FALSE,
353	13x	child_labels = "hidden"
354		)
355	13x	lyt_or <- analyze_colvars(
356	13x	lyt = lyt_or,
357	13x	afun = afun_lst[names(colvars_or$labels)],
358	13x	na_str = na_str,
359	13x	extra_args = extra_args
360		) %>%
361	13x	append_topleft("Baseline Risk Factors")
362
363		# Add analysis rows
364	13x	if ("analysis" %in% df$or$row_type) {
365	12x	lyt_or <- split_rows_by(
366	12x	lyt = lyt_or,
367	12x	var = "row_type",
368	12x	split_fun = keep_split_levels("analysis"),
369	12x	nested = FALSE,
370	12x	child_labels = "hidden"
371		)
372	12x	lyt_or <- split_rows_by(lyt = lyt_or, var = "var_label", nested = TRUE)
373	12x	lyt_or <- analyze_colvars(
374	12x	lyt = lyt_or,
375	12x	afun = afun_lst[names(colvars_or$labels)],
376	12x	na_str = na_str,
377	12x	inclNAs = TRUE,
378	12x	extra_args = extra_args
379		)
380		}
381
382	13x	table_or <- build_table(lyt_or, df = df$or)
383
384		# Join tables, add forest plot attributes
385	13x	n_tot_id <- match("n_tot", colvars_or$vars)
386	13x	if (is.null(table_prop)) {
387	1x	result <- table_or
388	1x	or_id <- match("or", colvars_or$vars)
389	1x	ci_id <- match("ci", colvars_or$vars)
390		} else {
391	12x	result <- cbind_rtables(table_or[, n_tot_id], table_prop, table_or[, -n_tot_id])
392	12x	or_id <- 1L + ncol(table_prop) + match("or", colvars_or$vars[-n_tot_id])
393	12x	ci_id <- 1L + ncol(table_prop) + match("ci", colvars_or$vars[-n_tot_id])
394	12x	n_tot_id <- 1L
395		}
396	13x	structure(
397	13x	result,
398	13x	forest_header = paste0(levels(df$prop$arm), "\nBetter"),
399	13x	col_x = or_id,
400	13x	col_ci = ci_id,
401	13x	col_symbol_size = n_tot_id
402		)
403		}
404
405		#' Labels for column variables in binary response by subgroup table
406		#'
407		#' @description `r lifecycle::badge("stable")`
408		#'
409		#' Internal function to check variables included in [tabulate_rsp_subgroups()] and create column labels.
410		#'
411		#' @inheritParams argument_convention
412		#' @inheritParams tabulate_rsp_subgroups
413		#'
414		#' @return A `list` of variables to tabulate and their labels.
415		#'
416		#' @export
417		d_rsp_subgroups_colvars <- function(vars,
418		conf_level = NULL,
419		method = NULL) {
420	22x	checkmate::assert_character(vars)
421	22x	checkmate::assert_subset(c("n_tot", "or", "ci"), vars)
422	22x	checkmate::assert_subset(
423	22x	vars,
424	22x	c("n", "n_rsp", "prop", "n_tot", "or", "ci", "pval")
425		)
426
427	22x	varlabels <- c(
428	22x	n = "n",
429	22x	n_rsp = "Responders",
430	22x	prop = "Response (%)",
431	22x	n_tot = "Total n",
432	22x	or = "Odds Ratio"
433		)
434	22x	colvars <- vars
435
436	22x	if ("ci" %in% colvars) {
437	22x	checkmate::assert_false(is.null(conf_level))
438
439	22x	varlabels <- c(
440	22x	varlabels,
441	22x	ci = paste0(100 * conf_level, "% CI")
442		)
443
444		# The `lcl`` variable is just a placeholder available in the analysis data,
445		# it is not acutally used in the tabulation.
446		# Variables used in the tabulation are lcl and ucl, see `a_response_subgroups` for details.
447	22x	colvars[colvars == "ci"] <- "lcl"
448		}
449
450	22x	if ("pval" %in% colvars) {
451	16x	varlabels <- c(
452	16x	varlabels,
453	16x	pval = method
454		)
455		}
456
457	22x	list(
458	22x	vars = colvars,
459	22x	labels = varlabels[vars]
460		)
461		}

1		#' Helper functions for tabulating biomarker effects on survival by subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions which are documented here separately to not confuse the user
6		#' when reading about the user-facing functions.
7		#'
8		#' @inheritParams survival_biomarkers_subgroups
9		#' @inheritParams argument_convention
10		#' @inheritParams fit_coxreg_multivar
11		#'
12		#' @examples
13		#' library(dplyr)
14		#' library(forcats)
15		#'
16		#' adtte <- tern_ex_adtte
17		#'
18		#' # Save variable labels before data processing steps.
19		#' adtte_labels <- formatters::var_labels(adtte, fill = FALSE)
20		#'
21		#' adtte_f <- adtte %>%
22		#' filter(PARAMCD == "OS") %>%
23		#' mutate(
24		#' AVALU = as.character(AVALU),
25		#' is_event = CNSR == 0
26		#' )
27		#' labels <- c("AVALU" = adtte_labels[["AVALU"]], "is_event" = "Event Flag")
28		#' formatters::var_labels(adtte_f)[names(labels)] <- labels
29		#'
30		#' @name h_survival_biomarkers_subgroups
31		NULL
32
33		#' @describeIn h_survival_biomarkers_subgroups Helps with converting the "survival" function variable list
34		#' to the "Cox regression" variable list. The reason is that currently there is an inconsistency between the variable
35		#' names accepted by `extract_survival_subgroups()` and `fit_coxreg_multivar()`.
36		#'
37		#' @param biomarker (`string`)\cr the name of the biomarker variable.
38		#'
39		#' @return
40		#' * `h_surv_to_coxreg_variables()` returns a named `list` of elements `time`, `event`, `arm`,
41		#' `covariates`, and `strata`.
42		#'
43		#' @examples
44		#' # This is how the variable list is converted internally.
45		#' h_surv_to_coxreg_variables(
46		#' variables = list(
47		#' tte = "AVAL",
48		#' is_event = "EVNT",
49		#' covariates = c("A", "B"),
50		#' strata = "D"
51		#' ),
52		#' biomarker = "AGE"
53		#' )
54		#'
55		#' @export
56		h_surv_to_coxreg_variables <- function(variables, biomarker) {
57	65x	checkmate::assert_list(variables)
58	65x	checkmate::assert_string(variables$tte)
59	65x	checkmate::assert_string(variables$is_event)
60	65x	checkmate::assert_string(biomarker)
61	65x	list(
62	65x	time = variables$tte,
63	65x	event = variables$is_event,
64	65x	arm = biomarker,
65	65x	covariates = variables$covariates,
66	65x	strata = variables$strata
67		)
68		}
69
70		#' @describeIn h_survival_biomarkers_subgroups Prepares estimates for number of events, patients and median survival
71		#' times, as well as hazard ratio estimates, confidence intervals and p-values, for multiple biomarkers
72		#' in a given single data set.
73		#' `variables` corresponds to names of variables found in `data`, passed as a named list and requires elements
74		#' `tte`, `is_event`, `biomarkers` (vector of continuous biomarker variables) and optionally `subgroups` and `strata`.
75		#'
76		#' @return
77		#' * `h_coxreg_mult_cont_df()` returns a `data.frame` containing estimates and statistics for the selected biomarkers.
78		#'
79		#' @examples
80		#' # For a single population, estimate separately the effects
81		#' # of two biomarkers.
82		#' df <- h_coxreg_mult_cont_df(
83		#' variables = list(
84		#' tte = "AVAL",
85		#' is_event = "is_event",
86		#' biomarkers = c("BMRKR1", "AGE"),
87		#' covariates = "SEX",
88		#' strata = c("STRATA1", "STRATA2")
89		#' ),
90		#' data = adtte_f
91		#' )
92		#' df
93		#'
94		#' # If the data set is empty, still the corresponding rows with missings are returned.
95		#' h_coxreg_mult_cont_df(
96		#' variables = list(
97		#' tte = "AVAL",
98		#' is_event = "is_event",
99		#' biomarkers = c("BMRKR1", "AGE"),
100		#' covariates = "REGION1",
101		#' strata = c("STRATA1", "STRATA2")
102		#' ),
103		#' data = adtte_f[NULL, ]
104		#' )
105		#'
106		#' @export
107		h_coxreg_mult_cont_df <- function(variables,
108		data,
109		control = control_coxreg()) {
110	33x	if ("strat" %in% names(variables)) {
111	!	warning(
112	!	"Warning: the `strat` element name of the `variables` list argument to `h_coxreg_mult_cont_df() ",
113	!	"was deprecated in tern 0.9.4.\n ",
114	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
115		)
116	!	variables[["strata"]] <- variables[["strat"]]
117		}
118
119	33x	assert_df_with_variables(data, variables)
120	33x	checkmate::assert_list(control, names = "named")
121	33x	checkmate::assert_character(variables$biomarkers, min.len = 1, any.missing = FALSE)
122	33x	conf_level <- control[["conf_level"]]
123	33x	pval_label <- paste0(
124		# the regex capitalizes the first letter of the string / senetence.
125	33x	"p-value (", gsub("(^[a-z])", "\\U\\1", trimws(control[["pval_method"]]), perl = TRUE), ")"
126		)
127		# If there is any data, run model, otherwise return empty results.
128	33x	if (nrow(data) > 0) {
129	32x	bm_cols <- match(variables$biomarkers, names(data))
130	32x	l_result <- lapply(variables$biomarkers, function(bm) {
131	64x	coxreg_list <- fit_coxreg_multivar(
132	64x	variables = h_surv_to_coxreg_variables(variables, bm),
133	64x	data = data,
134	64x	control = control
135		)
136	64x	result <- do.call(
137	64x	h_coxreg_multivar_extract,
138	64x	c(list(var = bm), coxreg_list[c("mod", "data", "control")])
139		)
140	64x	data_fit <- as.data.frame(as.matrix(coxreg_list$mod$y))
141	64x	data_fit$status <- as.logical(data_fit$status)
142	64x	median <- s_surv_time(
143	64x	df = data_fit,
144	64x	.var = "time",
145	64x	is_event = "status"
146	64x	)$median
147	64x	data.frame(
148		# Dummy column needed downstream to create a nested header.
149	64x	biomarker = bm,
150	64x	biomarker_label = formatters::var_labels(data[bm], fill = TRUE),
151	64x	n_tot = coxreg_list$mod$n,
152	64x	n_tot_events = coxreg_list$mod$nevent,
153	64x	median = as.numeric(median),
154	64x	result[1L, c("hr", "lcl", "ucl")],
155	64x	conf_level = conf_level,
156	64x	pval = result[1L, "pval"],
157	64x	pval_label = pval_label,
158	64x	stringsAsFactors = FALSE
159		)
160		})
161	32x	do.call(rbind, args = c(l_result, make.row.names = FALSE))
162		} else {
163	1x	data.frame(
164	1x	biomarker = variables$biomarkers,
165	1x	biomarker_label = formatters::var_labels(data[variables$biomarkers], fill = TRUE),
166	1x	n_tot = 0L,
167	1x	n_tot_events = 0L,
168	1x	median = NA,
169	1x	hr = NA,
170	1x	lcl = NA,
171	1x	ucl = NA,
172	1x	conf_level = conf_level,
173	1x	pval = NA,
174	1x	pval_label = pval_label,
175	1x	row.names = seq_along(variables$biomarkers),
176	1x	stringsAsFactors = FALSE
177		)
178		}
179		}
180
181		#' @describeIn h_survival_biomarkers_subgroups Prepares a single sub-table given a `df_sub` containing
182		#' the results for a single biomarker.
183		#'
184		#' @param df (`data.frame`)\cr results for a single biomarker, as part of what is
185		#' returned by [extract_survival_biomarkers()] (it needs a couple of columns which are
186		#' added by that high-level function relative to what is returned by [h_coxreg_mult_cont_df()],
187		#' see the example).
188		#'
189		#' @return
190		#' * `h_tab_surv_one_biomarker()` returns an `rtables` table object with the given statistics arranged in columns.
191		#'
192		#' @examples
193		#' # Starting from above `df`, zoom in on one biomarker and add required columns.
194		#' df1 <- df[1, ]
195		#' df1$subgroup <- "All patients"
196		#' df1$row_type <- "content"
197		#' df1$var <- "ALL"
198		#' df1$var_label <- "All patients"
199		#' h_tab_surv_one_biomarker(
200		#' df1,
201		#' vars = c("n_tot", "n_tot_events", "median", "hr", "ci", "pval"),
202		#' time_unit = "days"
203		#' )
204		#'
205		#' @export
206		h_tab_surv_one_biomarker <- function(df,
207		vars,
208		time_unit,
209		na_str = default_na_str(),
210		.indent_mods = 0L,
211		...) {
212	10x	afuns <- a_survival_subgroups(na_str = na_str)[vars]
213	10x	colvars <- d_survival_subgroups_colvars(
214	10x	vars,
215	10x	conf_level = df$conf_level[1],
216	10x	method = df$pval_label[1],
217	10x	time_unit = time_unit
218		)
219	10x	h_tab_one_biomarker(
220	10x	df = df,
221	10x	afuns = afuns,
222	10x	colvars = colvars,
223	10x	na_str = na_str,
224	10x	.indent_mods = .indent_mods,
225		...
226		)
227		}

1		#' Formatting functions
2		#'
3		#' See below for the list of formatting functions created in `tern` to work with `rtables`.
4		#'
5		#' Other available formats can be listed via [`formatters::list_valid_format_labels()`]. Additional
6		#' custom formats can be created via the [`formatters::sprintf_format()`] function.
7		#'
8		#' @family formatting functions
9		#' @name formatting_functions
10		NULL
11
12		#' Format fraction and percentage
13		#'
14		#' @description `r lifecycle::badge("stable")`
15		#'
16		#' Formats a fraction together with ratio in percent.
17		#'
18		#' @param x (named `integer`)\cr vector with elements `num` and `denom`.
19		#' @param ... not used. Required for `rtables` interface.
20		#'
21		#' @return A string in the format `num / denom (ratio %)`. If `num` is 0, the format is `num / denom`.
22		#'
23		#' @examples
24		#' format_fraction(x = c(num = 2L, denom = 3L))
25		#' format_fraction(x = c(num = 0L, denom = 3L))
26		#'
27		#' @family formatting functions
28		#' @export
29		format_fraction <- function(x, ...) {
30	4x	attr(x, "label") <- NULL
31
32	4x	checkmate::assert_vector(x)
33	4x	checkmate::assert_count(x["num"])
34	2x	checkmate::assert_count(x["denom"])
35
36	2x	result <- if (x["num"] == 0) {
37	1x	paste0(x["num"], "/", x["denom"])
38		} else {
39	1x	paste0(
40	1x	x["num"], "/", x["denom"],
41	1x	" (", round(x["num"] / x["denom"] * 100, 1), "%)"
42		)
43		}
44
45	2x	return(result)
46		}
47
48		#' Format fraction and percentage with fixed single decimal place
49		#'
50		#' @description `r lifecycle::badge("stable")`
51		#'
52		#' Formats a fraction together with ratio in percent with fixed single decimal place.
53		#' Includes trailing zero in case of whole number percentages to always keep one decimal place.
54		#'
55		#' @inheritParams format_fraction
56		#'
57		#' @return A string in the format `num / denom (ratio %)`. If `num` is 0, the format is `num / denom`.
58		#'
59		#' @examples
60		#' format_fraction_fixed_dp(x = c(num = 1L, denom = 2L))
61		#' format_fraction_fixed_dp(x = c(num = 1L, denom = 4L))
62		#' format_fraction_fixed_dp(x = c(num = 0L, denom = 3L))
63		#'
64		#' @family formatting functions
65		#' @export
66		format_fraction_fixed_dp <- function(x, ...) {
67	3x	attr(x, "label") <- NULL
68	3x	checkmate::assert_vector(x)
69	3x	checkmate::assert_count(x["num"])
70	3x	checkmate::assert_count(x["denom"])
71
72	3x	result <- if (x["num"] == 0) {
73	1x	paste0(x["num"], "/", x["denom"])
74		} else {
75	2x	paste0(
76	2x	x["num"], "/", x["denom"],
77	2x	" (", sprintf("%.1f", round(x["num"] / x["denom"] * 100, 1)), "%)"
78		)
79		}
80	3x	return(result)
81		}
82
83		#' Format count and fraction
84		#'
85		#' @description `r lifecycle::badge("stable")`
86		#'
87		#' Formats a count together with fraction with special consideration when count is `0`.
88		#'
89		#' @param x (`numeric(2)`)\cr vector of length 2 with count and fraction, respectively.
90		#' @param ... not used. Required for `rtables` interface.
91		#'
92		#' @return A string in the format `count (fraction %)`. If `count` is 0, the format is `0`.
93		#'
94		#' @examples
95		#' format_count_fraction(x = c(2, 0.6667))
96		#' format_count_fraction(x = c(0, 0))
97		#'
98		#' @family formatting functions
99		#' @export
100		format_count_fraction <- function(x, ...) {
101	3x	attr(x, "label") <- NULL
102
103	3x	if (any(is.na(x))) {
104	1x	return("NA")
105		}
106
107	2x	checkmate::assert_vector(x)
108	2x	checkmate::assert_integerish(x[1])
109	2x	assert_proportion_value(x[2], include_boundaries = TRUE)
110
111	2x	result <- if (x[1] == 0) {
112	1x	"0"
113		} else {
114	1x	paste0(x[1], " (", round(x[2] * 100, 1), "%)")
115		}
116
117	2x	return(result)
118		}
119
120		#' Format count and percentage with fixed single decimal place
121		#'
122		#' @description `r lifecycle::badge("experimental")`
123		#'
124		#' Formats a count together with fraction with special consideration when count is `0`.
125		#'
126		#' @inheritParams format_count_fraction
127		#'
128		#' @return A string in the format `count (fraction %)`. If `count` is 0, the format is `0`.
129		#'
130		#' @examples
131		#' format_count_fraction_fixed_dp(x = c(2, 0.6667))
132		#' format_count_fraction_fixed_dp(x = c(2, 0.5))
133		#' format_count_fraction_fixed_dp(x = c(0, 0))
134		#'
135		#' @family formatting functions
136		#' @export
137		format_count_fraction_fixed_dp <- function(x, ...) {
138	1408x	attr(x, "label") <- NULL
139
140	1408x	if (any(is.na(x))) {
141	!	return("NA")
142		}
143
144	1408x	checkmate::assert_vector(x)
145	1408x	checkmate::assert_integerish(x[1])
146	1408x	assert_proportion_value(x[2], include_boundaries = TRUE)
147
148	1408x	result <- if (x[1] == 0) {
149	195x	"0"
150	1408x	} else if (.is_equal_float(x[2], 1)) {
151	549x	sprintf("%d (100%%)", x[1])
152		} else {
153	664x	sprintf("%d (%.1f%%)", x[1], x[2] * 100)
154		}
155
156	1408x	return(result)
157		}
158
159		#' Format count and fraction with special case for count < 10
160		#'
161		#' @description `r lifecycle::badge("stable")`
162		#'
163		#' Formats a count together with fraction with special consideration when count is less than 10.
164		#'
165		#' @inheritParams format_count_fraction
166		#'
167		#' @return A string in the format `count (fraction %)`. If `count` is less than 10, only `count` is printed.
168		#'
169		#' @examples
170		#' format_count_fraction_lt10(x = c(275, 0.9673))
171		#' format_count_fraction_lt10(x = c(2, 0.6667))
172		#' format_count_fraction_lt10(x = c(9, 1))
173		#'
174		#' @family formatting functions
175		#' @export
176		format_count_fraction_lt10 <- function(x, ...) {
177	7x	attr(x, "label") <- NULL
178
179	7x	if (any(is.na(x))) {
180	1x	return("NA")
181		}
182
183	6x	checkmate::assert_vector(x)
184	6x	checkmate::assert_integerish(x[1])
185	6x	assert_proportion_value(x[2], include_boundaries = TRUE)
186
187	6x	result <- if (x[1] < 10) {
188	3x	paste0(x[1])
189		} else {
190	3x	paste0(x[1], " (", round(x[2] * 100, 1), "%)")
191		}
192
193	6x	return(result)
194		}
195
196		#' Format XX as a formatting function
197		#'
198		#' Translate a string where x and dots are interpreted as number place
199		#' holders, and others as formatting elements.
200		#'
201		#' @param str (`string`)\cr template.
202		#'
203		#' @return An `rtables` formatting function.
204		#'
205		#' @examples
206		#' test <- list(c(1.658, 0.5761), c(1e1, 785.6))
207		#'
208		#' z <- format_xx("xx (xx.x)")
209		#' sapply(test, z)
210		#'
211		#' z <- format_xx("xx.x - xx.x")
212		#' sapply(test, z)
213		#'
214		#' z <- format_xx("xx.x, incl. xx.x% NE")
215		#' sapply(test, z)
216		#'
217		#' @family formatting functions
218		#' @export
219		format_xx <- function(str) {
220		# Find position in the string.
221	1x	positions <- gregexpr(pattern = "x+\\.x+\|x+", text = str, perl = TRUE)
222	1x	x_positions <- regmatches(x = str, m = positions)[[1]]
223
224		# Roundings depends on the number of x behind [.].
225	1x	roundings <- lapply(
226	1x	X = x_positions,
227	1x	function(x) {
228	2x	y <- strsplit(split = "\\.", x = x)[[1]]
229	2x	rounding <- function(x) {
230	4x	round(x, digits = ifelse(length(y) > 1, nchar(y[2]), 0))
231		}
232	2x	return(rounding)
233		}
234		)
235
236	1x	rtable_format <- function(x, output) {
237	2x	values <- Map(y = x, fun = roundings, function(y, fun) fun(y))
238	2x	regmatches(x = str, m = positions)[[1]] <- values
239	2x	return(str)
240		}
241
242	1x	return(rtable_format)
243		}
244
245		#' Format numeric values by significant figures
246		#'
247		#' Format numeric values to print with a specified number of significant figures.
248		#'
249		#' @param sigfig (`integer(1)`)\cr number of significant figures to display.
250		#' @param format (`string`)\cr the format label (string) to apply when printing the value. Decimal
251		#' places in string are ignored in favor of formatting by significant figures. Formats options are:
252		#' `"xx"`, `"xx / xx"`, `"(xx, xx)"`, `"xx - xx"`, and `"xx (xx)"`.
253		#' @param num_fmt (`string`)\cr numeric format modifiers to apply to the value. Defaults to `"fg"` for
254		#' standard significant figures formatting - fixed (non-scientific notation) format (`"f"`)
255		#' and `sigfig` equal to number of significant figures instead of decimal places (`"g"`). See the
256		#' [formatC()] `format` argument for more options.
257		#'
258		#' @return An `rtables` formatting function.
259		#'
260		#' @examples
261		#' fmt_3sf <- format_sigfig(3)
262		#' fmt_3sf(1.658)
263		#' fmt_3sf(1e1)
264		#'
265		#' fmt_5sf <- format_sigfig(5)
266		#' fmt_5sf(0.57)
267		#' fmt_5sf(0.000025645)
268		#'
269		#' @family formatting functions
270		#' @export
271		format_sigfig <- function(sigfig, format = "xx", num_fmt = "fg") {
272	3x	checkmate::assert_integerish(sigfig)
273	3x	format <- gsub("xx\\.\|xx\\.x+", "xx", format)
274	3x	checkmate::assert_choice(format, c("xx", "xx / xx", "(xx, xx)", "xx - xx", "xx (xx)"))
275	3x	function(x, ...) {
276	!	if (!is.numeric(x)) stop("`format_sigfig` cannot be used for non-numeric values. Please choose another format.")
277	12x	num <- formatC(signif(x, digits = sigfig), digits = sigfig, format = num_fmt, flag = "#")
278	12x	num <- gsub("\\.$", "", num) # remove trailing "."
279
280	12x	format_value(num, format)
281		}
282		}
283
284		#' Format fraction with lower threshold
285		#'
286		#' @description `r lifecycle::badge("stable")`
287		#'
288		#' Formats a fraction when the second element of the input `x` is the fraction. It applies
289		#' a lower threshold, below which it is just stated that the fraction is smaller than that.
290		#'
291		#' @param threshold (`proportion`)\cr lower threshold.
292		#'
293		#' @return An `rtables` formatting function that takes numeric input `x` where the second
294		#' element is the fraction that is formatted. If the fraction is above or equal to the threshold,
295		#' then it is displayed in percentage. If it is positive but below the threshold, it returns,
296		#' e.g. "<1" if the threshold is `0.01`. If it is zero, then just "0" is returned.
297		#'
298		#' @examples
299		#' format_fun <- format_fraction_threshold(0.05)
300		#' format_fun(x = c(20, 0.1))
301		#' format_fun(x = c(2, 0.01))
302		#' format_fun(x = c(0, 0))
303		#'
304		#' @family formatting functions
305		#' @export
306		format_fraction_threshold <- function(threshold) {
307	1x	assert_proportion_value(threshold)
308	1x	string_below_threshold <- paste0("<", round(threshold * 100))
309	1x	function(x, ...) {
310	3x	assert_proportion_value(x[2], include_boundaries = TRUE)
311	3x	ifelse(
312	3x	x[2] > 0.01,
313	3x	round(x[2] * 100),
314	3x	ifelse(
315	3x	x[2] == 0,
316	3x	"0",
317	3x	string_below_threshold
318		)
319		)
320		}
321		}
322
323		#' Format extreme values
324		#'
325		#' @description `r lifecycle::badge("stable")`
326		#'
327		#' `rtables` formatting functions that handle extreme values.
328		#'
329		#' @param digits (`integer(1)`)\cr number of decimal places to display.
330		#'
331		#' @details For each input, apply a format to the specified number of `digits`. If the value is
332		#' below a threshold, it returns "<0.01" e.g. if the number of `digits` is 2. If the value is
333		#' above a threshold, it returns ">999.99" e.g. if the number of `digits` is 2.
334		#' If it is zero, then returns "0.00".
335		#'
336		#' @family formatting functions
337		#' @name extreme_format
338		NULL
339
340		#' @describeIn extreme_format Internal helper function to calculate the threshold and create formatted strings
341		#' used in Formatting Functions. Returns a list with elements `threshold` and `format_string`.
342		#'
343		#' @return
344		#' * `h_get_format_threshold()` returns a `list` of 2 elements: `threshold`, with `low` and `high` thresholds,
345		#' and `format_string`, with thresholds formatted as strings.
346		#'
347		#' @examples
348		#' h_get_format_threshold(2L)
349		#'
350		#' @export
351		h_get_format_threshold <- function(digits = 2L) {
352	2113x	checkmate::assert_integerish(digits)
353
354	2113x	low_threshold <- 1 / (10 ^ digits) # styler: off
355	2113x	high_threshold <- 1000 - (1 / (10 ^ digits)) # styler: off
356
357	2113x	string_below_threshold <- paste0("<", low_threshold)
358	2113x	string_above_threshold <- paste0(">", high_threshold)
359
360	2113x	list(
361	2113x	"threshold" = c(low = low_threshold, high = high_threshold),
362	2113x	"format_string" = c(low = string_below_threshold, high = string_above_threshold)
363		)
364		}
365
366		#' @describeIn extreme_format Internal helper function to apply a threshold format to a value.
367		#' Creates a formatted string to be used in Formatting Functions.
368		#'
369		#' @param x (`numeric(1)`)\cr value to format.
370		#'
371		#' @return
372		#' * `h_format_threshold()` returns the given value, or if the value is not within the digit threshold the relation
373		#' of the given value to the digit threshold, as a formatted string.
374		#'
375		#' @examples
376		#' h_format_threshold(0.001)
377		#' h_format_threshold(1000)
378		#'
379		#' @export
380		h_format_threshold <- function(x, digits = 2L) {
381	2115x	if (is.na(x)) {
382	4x	return(x)
383		}
384
385	2111x	checkmate::assert_numeric(x, lower = 0)
386
387	2111x	l_fmt <- h_get_format_threshold(digits)
388
389	2111x	result <- if (x < l_fmt$threshold["low"] && 0 < x) {
390	44x	l_fmt$format_string["low"]
391	2111x	} else if (x > l_fmt$threshold["high"]) {
392	99x	l_fmt$format_string["high"]
393		} else {
394	1968x	sprintf(fmt = paste0("%.", digits, "f"), x)
395		}
396
397	2111x	unname(result)
398		}
399
400		#' Format a single extreme value
401		#'
402		#' @description `r lifecycle::badge("stable")`
403		#'
404		#' Create a formatting function for a single extreme value.
405		#'
406		#' @inheritParams extreme_format
407		#'
408		#' @return An `rtables` formatting function that uses threshold `digits` to return a formatted extreme value.
409		#'
410		#' @examples
411		#' format_fun <- format_extreme_values(2L)
412		#' format_fun(x = 0.127)
413		#' format_fun(x = Inf)
414		#' format_fun(x = 0)
415		#' format_fun(x = 0.009)
416		#'
417		#' @family formatting functions
418		#' @export
419		format_extreme_values <- function(digits = 2L) {
420	63x	function(x, ...) {
421	657x	checkmate::assert_scalar(x, na.ok = TRUE)
422
423	657x	h_format_threshold(x = x, digits = digits)
424		}
425		}
426
427		#' Format extreme values part of a confidence interval
428		#'
429		#' @description `r lifecycle::badge("stable")`
430		#'
431		#' Formatting Function for extreme values part of a confidence interval. Values
432		#' are formatted as e.g. "(xx.xx, xx.xx)" if the number of `digits` is 2.
433		#'
434		#' @inheritParams extreme_format
435		#'
436		#' @return An `rtables` formatting function that uses threshold `digits` to return a formatted extreme
437		#' values confidence interval.
438		#'
439		#' @examples
440		#' format_fun <- format_extreme_values_ci(2L)
441		#' format_fun(x = c(0.127, Inf))
442		#' format_fun(x = c(0, 0.009))
443		#'
444		#' @family formatting functions
445		#' @export
446		format_extreme_values_ci <- function(digits = 2L) {
447	71x	function(x, ...) {
448	726x	checkmate::assert_vector(x, len = 2)
449	726x	l_result <- h_format_threshold(x = x[1], digits = digits)
450	726x	h_result <- h_format_threshold(x = x[2], digits = digits)
451
452	726x	paste0("(", l_result, ", ", h_result, ")")
453		}
454		}
455
456		#' Format automatically using data significant digits
457		#'
458		#' @description `r lifecycle::badge("stable")`
459		#'
460		#' Formatting function for the majority of default methods used in [analyze_vars()].
461		#' For non-derived values, the significant digits of data is used (e.g. range), while derived
462		#' values have one more digits (measure of location and dispersion like mean, standard deviation).
463		#' This function can be called internally with "auto" like, for example,
464		#' `.formats = c("mean" = "auto")`. See details to see how this works with the inner function.
465		#'
466		#' @param dt_var (`numeric`)\cr variable data the statistics were calculated from. Used only to
467		#' find significant digits. In [analyze_vars] this comes from `.df_row` (see
468		#' [rtables::additional_fun_params]), and it is the row data after the above row splits. No
469		#' column split is considered.
470		#' @param x_stat (`string`)\cr string indicating the current statistical method used.
471		#'
472		#' @return A string that `rtables` prints in a table cell.
473		#'
474		#' @details
475		#' The internal function is needed to work with `rtables` default structure for
476		#' format functions, i.e. `function(x, ...)`, where is x are results from statistical evaluation.
477		#' It can be more than one element (e.g. for `.stats = "mean_sd"`).
478		#'
479		#' @examples
480		#' x_todo <- c(0.001, 0.2, 0.0011000, 3, 4)
481		#' res <- c(mean(x_todo[1:3]), sd(x_todo[1:3]))
482		#'
483		#' # x is the result coming into the formatting function -> res!!
484		#' format_auto(dt_var = x_todo, x_stat = "mean_sd")(x = res)
485		#' format_auto(x_todo, "range")(x = range(x_todo))
486		#' no_sc_x <- c(0.0000001, 1)
487		#' format_auto(no_sc_x, "range")(x = no_sc_x)
488		#'
489		#' @family formatting functions
490		#' @export
491		format_auto <- function(dt_var, x_stat) {
492	14x	function(x = "", ...) {
493	31x	checkmate::assert_numeric(x, min.len = 1)
494	31x	checkmate::assert_numeric(dt_var, min.len = 1)
495		# Defaults - they may be a param in the future
496	31x	der_stats <- c(
497	31x	"mean", "sd", "se", "median", "geom_mean", "quantiles", "iqr",
498	31x	"mean_sd", "mean_se", "mean_se", "mean_ci", "mean_sei", "mean_sdi",
499	31x	"median_ci"
500		)
501	31x	nonder_stats <- c("n", "range", "min", "max")
502
503		# Safenet for miss-modifications
504	31x	stopifnot(length(intersect(der_stats, nonder_stats)) == 0) # nolint
505	31x	checkmate::assert_choice(x_stat, c(der_stats, nonder_stats))
506
507		# Finds the max number of digits in data
508	31x	detect_dig <- vapply(dt_var, count_decimalplaces, FUN.VALUE = numeric(1)) %>%
509	31x	max()
510
511	31x	if (x_stat %in% der_stats) {
512	19x	detect_dig <- detect_dig + 1
513		}
514
515		# Render input
516	31x	str_vals <- formatC(x, digits = detect_dig, format = "f")
517	31x	def_fmt <- get_formats_from_stats(x_stat)[[x_stat]]
518	31x	str_fmt <- str_extract(def_fmt, invert = FALSE)[[1]]
519	31x	if (length(str_fmt) != length(str_vals)) {
520	2x	stop(
521	2x	"Number of inserted values as result (", length(str_vals),
522	2x	") is not the same as there should be in the default tern formats for ",
523	2x	x_stat, " (-> ", def_fmt, " needs ", length(str_fmt), " values). ",
524	2x	"See tern_default_formats to check all of them."
525		)
526		}
527
528		# Squashing them together
529	29x	inv_str_fmt <- str_extract(def_fmt, invert = TRUE)[[1]]
530	29x	stopifnot(length(inv_str_fmt) == length(str_vals) + 1) # nolint
531
532	29x	out <- vector("character", length = length(inv_str_fmt) + length(str_vals))
533	29x	is_even <- seq_along(out) %% 2 == 0
534	29x	out[is_even] <- str_vals
535	29x	out[!is_even] <- inv_str_fmt
536
537	29x	return(paste0(out, collapse = ""))
538		}
539		}
540
541		# Utility function that could be useful in general
542		str_extract <- function(string, pattern = "xx\|xx\\.\|xx\\.x+", invert = FALSE) {
543	60x	regmatches(string, gregexpr(pattern, string), invert = invert)
544		}
545
546		# Helper function
547		count_decimalplaces <- function(dec) {
548	226x	if (is.na(dec)) {
549	6x	return(0)
550	220x	} else if (abs(dec - round(dec)) > .Machine$double.eps^0.5) { # For precision
551	161x	nchar(strsplit(format(dec, scientific = FALSE, trim = FALSE), ".", fixed = TRUE)[[1]][[2]])
552		} else {
553	59x	return(0)
554		}
555		}
556
557		#' Apply automatic formatting
558		#'
559		#' Checks if any of the listed formats in `.formats` are `"auto"`, and replaces `"auto"` with
560		#' the correct implementation of `format_auto` for the given statistics, data, and variable.
561		#'
562		#' @inheritParams argument_convention
563		#' @param x_stats (named `list`)\cr a named list of statistics where each element corresponds
564		#' to an element in `.formats`, with matching names.
565		#'
566		#' @keywords internal
567		apply_auto_formatting <- function(.formats, x_stats, .df_row, .var) {
568	554x	is_auto_fmt <- vapply(.formats, function(ii) is.character(ii) && ii == "auto", logical(1))
569	554x	if (any(is_auto_fmt)) {
570	6x	auto_stats <- x_stats[is_auto_fmt]
571	6x	var_df <- .df_row[[.var]] # xxx this can be extended for the WHOLE data or single facets
572	6x	.formats[is_auto_fmt] <- lapply(names(auto_stats), format_auto, dt_var = var_df)
573		}
574	554x	.formats
575		}

1		#' Count occurrences
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [count_occurrences()] creates a layout element to calculate occurrence counts for patients.
6		#'
7		#' This function analyzes the variable(s) supplied to `vars` and returns a table of occurrence counts for
8		#' each unique value (or level) of the variable(s). This variable (or variables) must be
9		#' non-numeric. The `id` variable is used to indicate unique subject identifiers (defaults to `USUBJID`).
10		#'
11		#' If there are multiple occurrences of the same value recorded for a patient, the value is only counted once.
12		#'
13		#' The summarize function [summarize_occurrences()] performs the same function as [count_occurrences()] except it
14		#' creates content rows, not data rows, to summarize the current table row/column context and operates on the level of
15		#' the latest row split or the root of the table if no row splits have occurred.
16		#'
17		#' @inheritParams argument_convention
18		#' @param drop (`flag`)\cr whether non-appearing occurrence levels should be dropped from the resulting table.
19		#' Note that in that case the remaining occurrence levels in the table are sorted alphabetically.
20		#' @param .stats (`character`)\cr statistics to select for the table.
21		#'
22		#' Options are: ``r shQuote(get_stats("count_occurrences"), type = "sh")``
23		#'
24		#' @note By default, occurrences which don't appear in a given row split are dropped from the table and
25		#' the occurrences in the table are sorted alphabetically per row split. Therefore, the corresponding layout
26		#' needs to use `split_fun = drop_split_levels` in the `split_rows_by` calls. Use `drop = FALSE` if you would
27		#' like to show all occurrences.
28		#'
29		#' @examples
30		#' library(dplyr)
31		#' df <- data.frame(
32		#' USUBJID = as.character(c(
33		#' 1, 1, 2, 4, 4, 4,
34		#' 6, 6, 6, 7, 7, 8
35		#' )),
36		#' MHDECOD = c(
37		#' "MH1", "MH2", "MH1", "MH1", "MH1", "MH3",
38		#' "MH2", "MH2", "MH3", "MH1", "MH2", "MH4"
39		#' ),
40		#' ARM = rep(c("A", "B"), each = 6),
41		#' SEX = c("F", "F", "M", "M", "M", "M", "F", "F", "F", "M", "M", "F")
42		#' )
43		#' df_adsl <- df %>%
44		#' select(USUBJID, ARM) %>%
45		#' unique()
46		#'
47		#' @name count_occurrences
48		#' @order 1
49		NULL
50
51		#' @describeIn count_occurrences Statistics function which counts number of patients that report an
52		#' occurrence.
53		#'
54		#' @param denom (`string`)\cr choice of denominator for proportion. Options are:
55		#' * `N_col`: total number of patients in this column across rows.
56		#' * `n`: number of patients with any occurrences.
57		#' * `N_row`: total number of patients in this row across columns.
58		#'
59		#' @return
60		#' * `s_count_occurrences()` returns a list with:
61		#' * `count`: list of counts with one element per occurrence.
62		#' * `count_fraction`: list of counts and fractions with one element per occurrence.
63		#' * `fraction`: list of numerators and denominators with one element per occurrence.
64		#'
65		#' @examples
66		#' # Count unique occurrences per subject.
67		#' s_count_occurrences(
68		#' df,
69		#' .N_col = 4L,
70		#' .N_row = 4L,
71		#' .df_row = df,
72		#' .var = "MHDECOD",
73		#' id = "USUBJID"
74		#' )
75		#'
76		#' @export
77		s_count_occurrences <- function(df,
78		denom = c("N_col", "n", "N_row"),
79		.N_col, # nolint
80		.N_row, # nolint
81		.df_row,
82		drop = TRUE,
83		.var = "MHDECOD",
84		id = "USUBJID") {
85	126x	checkmate::assert_flag(drop)
86	126x	assert_df_with_variables(df, list(range = .var, id = id))
87	126x	checkmate::assert_count(.N_col)
88	126x	checkmate::assert_multi_class(df[[.var]], classes = c("factor", "character"))
89	126x	checkmate::assert_multi_class(df[[id]], classes = c("factor", "character"))
90
91	126x	occurrences <- if (drop) {
92		# Note that we don't try to preserve original level order here since a) that would required
93		# more time to look up in large original levels and b) that would fail for character input variable.
94	115x	occurrence_levels <- sort(unique(.df_row[[.var]]))
95	115x	if (length(occurrence_levels) == 0) {
96	1x	stop(
97	1x	"no empty `.df_row` input allowed when `drop = TRUE`,",
98	1x	" please use `split_fun = drop_split_levels` in the `rtables` `split_rows_by` calls"
99		)
100		}
101	114x	factor(df[[.var]], levels = occurrence_levels)
102		} else {
103	11x	df[[.var]]
104		}
105	125x	ids <- factor(df[[id]])
106	125x	denom <- match.arg(denom) %>%
107	125x	switch(
108	125x	n = nlevels(ids),
109	125x	N_row = .N_row,
110	125x	N_col = .N_col
111		)
112	125x	has_occurrence_per_id <- table(occurrences, ids) > 0
113	125x	n_ids_per_occurrence <- as.list(rowSums(has_occurrence_per_id))
114	125x	cur_count_fraction <- lapply(
115	125x	n_ids_per_occurrence,
116	125x	function(i, denom) {
117	514x	if (i == 0 && denom == 0) {
118	!	c(0, 0)
119		} else {
120	514x	c(i, i / denom)
121		}
122		},
123	125x	denom = denom
124		)
125
126	125x	list(
127	125x	count = n_ids_per_occurrence,
128	125x	count_fraction = cur_count_fraction,
129	125x	count_fraction_fixed_dp = cur_count_fraction,
130	125x	fraction = lapply(
131	125x	n_ids_per_occurrence,
132	125x	function(i, denom) c("num" = i, "denom" = denom),
133	125x	denom = denom
134		)
135		)
136		}
137
138		#' @describeIn count_occurrences Formatted analysis function which is used as `afun`
139		#' in `count_occurrences()`.
140		#'
141		#' @return
142		#' * `a_count_occurrences()` returns the corresponding list with formatted [rtables::CellValue()].
143		#'
144		#' @examples
145		#' a_count_occurrences(
146		#' df,
147		#' .N_col = 4L,
148		#' .df_row = df,
149		#' .var = "MHDECOD",
150		#' id = "USUBJID"
151		#' )
152		#'
153		#' @export
154		a_count_occurrences <- function(df,
155		labelstr = "",
156		id = "USUBJID",
157		denom = c("N_col", "n", "N_row"),
158		drop = TRUE,
159		.N_col, # nolint
160		.N_row, # nolint
161		.var = NULL,
162		.df_row = NULL,
163		.stats = NULL,
164		.formats = NULL,
165		.labels = NULL,
166		.indent_mods = NULL,
167		na_str = default_na_str()) {
168	85x	denom <- match.arg(denom)
169	85x	x_stats <- s_count_occurrences(
170	85x	df = df, denom = denom, .N_col = .N_col, .N_row = .N_row, .df_row = .df_row, drop = drop, .var = .var, id = id
171		)
172	85x	if (is.null(unlist(x_stats))) {
173	3x	return(NULL)
174		}
175
176		# Fill in with formatting defaults if needed
177	82x	.stats <- get_stats("count_occurrences", stats_in = .stats)
178	82x	x_stats <- x_stats[.stats]
179	82x	levels_per_stats <- lapply(x_stats, names)
180	82x	.formats <- get_formats_from_stats(.stats, .formats, levels_per_stats)
181	82x	.labels <- get_labels_from_stats(.stats, .labels, levels_per_stats)
182	82x	.indent_mods <- get_indents_from_stats(.stats, .indent_mods, levels_per_stats)
183
184		# Unlist stats
185	82x	x_stats <- x_stats %>% .unlist_keep_nulls()
186
187		# Auto format handling
188	82x	.formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)
189
190	82x	in_rows(
191	82x	.list = x_stats,
192	82x	.formats = .formats,
193	82x	.names = .labels %>% .unlist_keep_nulls(),
194	82x	.labels = .labels %>% .unlist_keep_nulls(),
195	82x	.indent_mods = .indent_mods %>% .unlist_keep_nulls(),
196	82x	.format_na_strs = na_str
197		)
198		}
199
200		#' @describeIn count_occurrences Layout-creating function which can take statistics function arguments
201		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
202		#'
203		#' @return
204		#' * `count_occurrences()` returns a layout object suitable for passing to further layouting functions,
205		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
206		#' the statistics from `s_count_occurrences()` to the table layout.
207		#'
208		#' @examples
209		#' # Create table layout
210		#' lyt <- basic_table() %>%
211		#' split_cols_by("ARM") %>%
212		#' add_colcounts() %>%
213		#' count_occurrences(vars = "MHDECOD", .stats = c("count_fraction"))
214		#'
215		#' # Apply table layout to data and produce `rtable` object
216		#' tbl <- lyt %>%
217		#' build_table(df, alt_counts_df = df_adsl) %>%
218		#' prune_table()
219		#'
220		#' tbl
221		#'
222		#' @export
223		#' @order 2
224		count_occurrences <- function(lyt,
225		vars,
226		id = "USUBJID",
227		drop = TRUE,
228		var_labels = vars,
229		show_labels = "hidden",
230		riskdiff = FALSE,
231		na_str = default_na_str(),
232		nested = TRUE,
233		...,
234		table_names = vars,
235		.stats = "count_fraction_fixed_dp",
236		.formats = NULL,
237		.labels = NULL,
238		.indent_mods = NULL) {
239	9x	checkmate::assert_flag(riskdiff)
240
241	9x	extra_args <- list(
242	9x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
243		)
244	9x	s_args <- list(id = id, drop = drop, ...)
245
246	9x	if (isFALSE(riskdiff)) {
247	6x	extra_args <- c(extra_args, s_args)
248		} else {
249	3x	extra_args <- c(
250	3x	extra_args,
251	3x	list(
252	3x	afun = list("s_count_occurrences" = a_count_occurrences),
253	3x	s_args = s_args
254		)
255		)
256		}
257
258	9x	analyze(
259	9x	lyt = lyt,
260	9x	vars = vars,
261	9x	afun = ifelse(isFALSE(riskdiff), a_count_occurrences, afun_riskdiff),
262	9x	var_labels = var_labels,
263	9x	show_labels = show_labels,
264	9x	table_names = table_names,
265	9x	na_str = na_str,
266	9x	nested = nested,
267	9x	extra_args = extra_args
268		)
269		}
270
271		#' @describeIn count_occurrences Layout-creating function which can take content function arguments
272		#' and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
273		#'
274		#' @return
275		#' * `summarize_occurrences()` returns a layout object suitable for passing to further layouting functions,
276		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
277		#' containing the statistics from `s_count_occurrences()` to the table layout.
278		#'
279		#' @examples
280		#' # Layout creating function with custom format.
281		#' basic_table() %>%
282		#' add_colcounts() %>%
283		#' split_rows_by("SEX", child_labels = "visible") %>%
284		#' summarize_occurrences(
285		#' var = "MHDECOD",
286		#' .formats = c("count_fraction" = "xx.xx (xx.xx%)")
287		#' ) %>%
288		#' build_table(df, alt_counts_df = df_adsl)
289		#'
290		#' @export
291		#' @order 3
292		summarize_occurrences <- function(lyt,
293		var,
294		id = "USUBJID",
295		drop = TRUE,
296		riskdiff = FALSE,
297		na_str = default_na_str(),
298		...,
299		.stats = "count_fraction_fixed_dp",
300		.formats = NULL,
301		.indent_mods = NULL,
302		.labels = NULL) {
303	5x	checkmate::assert_flag(riskdiff)
304
305	5x	extra_args <- list(
306	5x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
307		)
308	5x	s_args <- list(id = id, drop = drop, ...)
309
310	5x	if (isFALSE(riskdiff)) {
311	1x	extra_args <- c(extra_args, s_args)
312		} else {
313	4x	extra_args <- c(
314	4x	extra_args,
315	4x	list(
316	4x	afun = list("s_count_occurrences" = a_count_occurrences),
317	4x	s_args = s_args
318		)
319		)
320		}
321
322	5x	summarize_row_groups(
323	5x	lyt = lyt,
324	5x	var = var,
325	5x	cfun = ifelse(isFALSE(riskdiff), a_count_occurrences, afun_riskdiff),
326	5x	na_str = na_str,
327	5x	extra_args = extra_args
328		)
329		}

1		#' Individual patient plots
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Line plot(s) displaying trend in patients' parameter values over time is rendered.
6		#' Patients' individual baseline values can be added to the plot(s) as reference.
7		#'
8		#' @inheritParams argument_convention
9		#' @param xvar (`string`)\cr time point variable to be plotted on x-axis.
10		#' @param yvar (`string`)\cr continuous analysis variable to be plotted on y-axis.
11		#' @param xlab (`string`)\cr plot label for x-axis.
12		#' @param ylab (`string`)\cr plot label for y-axis.
13		#' @param id_var (`string`)\cr variable used as patient identifier.
14		#' @param title (`string`)\cr title for plot.
15		#' @param subtitle (`string`)\cr subtitle for plot.
16		#' @param add_baseline_hline (`flag`)\cr adds horizontal line at baseline y-value on
17		#' plot when `TRUE`.
18		#' @param yvar_baseline (`string`)\cr variable with baseline values only.
19		#' Ignored when `add_baseline_hline` is `FALSE`.
20		#' @param ggtheme (`theme`)\cr optional graphical theme function as provided
21		#' by `ggplot2` to control outlook of plot. Use `ggplot2::theme()` to tweak the display.
22		#' @param plotting_choices (`string`)\cr specifies options for displaying
23		#' plots. Must be one of `"all_in_one"`, `"split_by_max_obs"`, or `"separate_by_obs"`.
24		#' @param max_obs_per_plot (`integer(1)`)\cr number of observations to be plotted on one
25		#' plot. Ignored if `plotting_choices` is not `"separate_by_obs"`.
26		#' @param caption (`string`)\cr optional caption below the plot.
27		#' @param col (`character`)\cr line colors.
28		#'
29		#' @seealso Relevant helper function [h_g_ipp()].
30		#'
31		#' @name g_ipp
32		#' @aliases individual_patient_plot
33		NULL
34
35		#' Helper function to create simple line plot over time
36		#'
37		#' @description `r lifecycle::badge("stable")`
38		#'
39		#' Function that generates a simple line plot displaying parameter trends over time.
40		#'
41		#' @inheritParams argument_convention
42		#' @inheritParams g_ipp
43		#'
44		#' @return A `ggplot` line plot.
45		#'
46		#' @seealso [g_ipp()] which uses this function.
47		#'
48		#' @examples
49		#' library(dplyr)
50		#'
51		#' # Select a small sample of data to plot.
52		#' adlb <- tern_ex_adlb %>%
53		#' filter(PARAMCD == "ALT", !(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
54		#' slice(1:36)
55		#'
56		#' p <- h_g_ipp(
57		#' df = adlb,
58		#' xvar = "AVISIT",
59		#' yvar = "AVAL",
60		#' xlab = "Visit",
61		#' id_var = "USUBJID",
62		#' ylab = "SGOT/ALT (U/L)",
63		#' add_baseline_hline = TRUE
64		#' )
65		#' p
66		#'
67		#' @export
68		h_g_ipp <- function(df,
69		xvar,
70		yvar,
71		xlab,
72		ylab,
73		id_var,
74		title = "Individual Patient Plots",
75		subtitle = "",
76		caption = NULL,
77		add_baseline_hline = FALSE,
78		yvar_baseline = "BASE",
79		ggtheme = nestcolor::theme_nest(),
80		col = NULL) {
81	13x	checkmate::assert_string(xvar)
82	13x	checkmate::assert_string(yvar)
83	13x	checkmate::assert_string(yvar_baseline)
84	13x	checkmate::assert_string(id_var)
85	13x	checkmate::assert_string(xlab)
86	13x	checkmate::assert_string(ylab)
87	13x	checkmate::assert_string(title)
88	13x	checkmate::assert_string(subtitle)
89	13x	checkmate::assert_subset(c(xvar, yvar, yvar_baseline, id_var), colnames(df))
90	13x	checkmate::assert_data_frame(df)
91	13x	checkmate::assert_flag(add_baseline_hline)
92	13x	checkmate::assert_character(col, null.ok = TRUE)
93
94	13x	p <- ggplot2::ggplot(
95	13x	data = df,
96	13x	mapping = ggplot2::aes(
97	13x	x = .data[[xvar]],
98	13x	y = .data[[yvar]],
99	13x	group = .data[[id_var]],
100	13x	colour = .data[[id_var]]
101		)
102		) +
103	13x	ggplot2::geom_line(linewidth = 0.4) +
104	13x	ggplot2::geom_point(size = 2) +
105	13x	ggplot2::labs(
106	13x	x = xlab,
107	13x	y = ylab,
108	13x	title = title,
109	13x	subtitle = subtitle,
110	13x	caption = caption
111		) +
112	13x	ggtheme
113
114	13x	if (add_baseline_hline) {
115	12x	baseline_df <- df[, c(id_var, yvar_baseline)]
116	12x	baseline_df <- unique(baseline_df)
117
118	12x	p <- p +
119	12x	ggplot2::geom_hline(
120	12x	data = baseline_df,
121	12x	mapping = ggplot2::aes(
122	12x	yintercept = .data[[yvar_baseline]],
123	12x	colour = .data[[id_var]]
124		),
125	12x	linetype = "dotdash",
126	12x	linewidth = 0.4
127		) +
128	12x	ggplot2::geom_text(
129	12x	data = baseline_df,
130	12x	mapping = ggplot2::aes(
131	12x	x = 1,
132	12x	y = .data[[yvar_baseline]],
133	12x	label = .data[[id_var]],
134	12x	colour = .data[[id_var]]
135		),
136	12x	nudge_y = 0.025 * (max(df[, yvar], na.rm = TRUE) - min(df[, yvar], na.rm = TRUE)),
137	12x	vjust = "right",
138	12x	size = 2
139		)
140
141	12x	if (!is.null(col)) {
142	1x	p <- p +
143	1x	ggplot2::scale_color_manual(values = col)
144		}
145		}
146	13x	p
147		}
148
149		#' @describeIn g_ipp Plotting function for individual patient plots which, depending on user
150		#' preference, renders a single graphic or compiles a list of graphics that show trends in individual's parameter
151		#' values over time.
152		#'
153		#' @return A `ggplot` object or a list of `ggplot` objects.
154		#'
155		#' @examples
156		#' library(dplyr)
157		#'
158		#' # Select a small sample of data to plot.
159		#' adlb <- tern_ex_adlb %>%
160		#' filter(PARAMCD == "ALT", !(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
161		#' slice(1:36)
162		#'
163		#' plot_list <- g_ipp(
164		#' df = adlb,
165		#' xvar = "AVISIT",
166		#' yvar = "AVAL",
167		#' xlab = "Visit",
168		#' ylab = "SGOT/ALT (U/L)",
169		#' title = "Individual Patient Plots",
170		#' add_baseline_hline = TRUE,
171		#' plotting_choices = "split_by_max_obs",
172		#' max_obs_per_plot = 5
173		#' )
174		#' plot_list
175		#'
176		#' @export
177		g_ipp <- function(df,
178		xvar,
179		yvar,
180		xlab,
181		ylab,
182		id_var = "USUBJID",
183		title = "Individual Patient Plots",
184		subtitle = "",
185		caption = NULL,
186		add_baseline_hline = FALSE,
187		yvar_baseline = "BASE",
188		ggtheme = nestcolor::theme_nest(),
189		plotting_choices = c("all_in_one", "split_by_max_obs", "separate_by_obs"),
190		max_obs_per_plot = 4,
191		col = NULL) {
192	3x	checkmate::assert_count(max_obs_per_plot)
193	3x	checkmate::assert_subset(plotting_choices, c("all_in_one", "split_by_max_obs", "separate_by_obs"))
194	3x	checkmate::assert_character(col, null.ok = TRUE)
195
196	3x	plotting_choices <- match.arg(plotting_choices)
197
198	3x	if (plotting_choices == "all_in_one") {
199	1x	p <- h_g_ipp(
200	1x	df = df,
201	1x	xvar = xvar,
202	1x	yvar = yvar,
203	1x	xlab = xlab,
204	1x	ylab = ylab,
205	1x	id_var = id_var,
206	1x	title = title,
207	1x	subtitle = subtitle,
208	1x	caption = caption,
209	1x	add_baseline_hline = add_baseline_hline,
210	1x	yvar_baseline = yvar_baseline,
211	1x	ggtheme = ggtheme,
212	1x	col = col
213		)
214
215	1x	return(p)
216	2x	} else if (plotting_choices == "split_by_max_obs") {
217	1x	id_vec <- unique(df[[id_var]])
218	1x	id_list <- split(
219	1x	id_vec,
220	1x	rep(1:ceiling(length(id_vec) / max_obs_per_plot),
221	1x	each = max_obs_per_plot,
222	1x	length.out = length(id_vec)
223		)
224		)
225
226	1x	df_list <- list()
227	1x	plot_list <- list()
228
229	1x	for (i in seq_along(id_list)) {
230	2x	df_list[[i]] <- df[df[[id_var]] %in% id_list[[i]], ]
231
232	2x	plots <- h_g_ipp(
233	2x	df = df_list[[i]],
234	2x	xvar = xvar,
235	2x	yvar = yvar,
236	2x	xlab = xlab,
237	2x	ylab = ylab,
238	2x	id_var = id_var,
239	2x	title = title,
240	2x	subtitle = subtitle,
241	2x	caption = caption,
242	2x	add_baseline_hline = add_baseline_hline,
243	2x	yvar_baseline = yvar_baseline,
244	2x	ggtheme = ggtheme,
245	2x	col = col
246		)
247
248	2x	plot_list[[i]] <- plots
249		}
250	1x	return(plot_list)
251		} else {
252	1x	ind_df <- split(df, df[[id_var]])
253	1x	plot_list <- lapply(
254	1x	ind_df,
255	1x	function(x) {
256	8x	h_g_ipp(
257	8x	df = x,
258	8x	xvar = xvar,
259	8x	yvar = yvar,
260	8x	xlab = xlab,
261	8x	ylab = ylab,
262	8x	id_var = id_var,
263	8x	title = title,
264	8x	subtitle = subtitle,
265	8x	caption = caption,
266	8x	add_baseline_hline = add_baseline_hline,
267	8x	yvar_baseline = yvar_baseline,
268	8x	ggtheme = ggtheme,
269	8x	col = col
270		)
271		}
272		)
273
274	1x	return(plot_list)
275		}
276		}

1		#' Additional assertions to use with `checkmate`
2		#'
3		#' Additional assertion functions which can be used together with the `checkmate` package.
4		#'
5		#' @inheritParams checkmate::assert_factor
6		#' @param x (`any`)\cr object to test.
7		#' @param df (`data.frame`)\cr data set to test.
8		#' @param variables (named `list` of `character`)\cr list of variables to test.
9		#' @param include_boundaries (`flag`)\cr whether to include boundaries when testing
10		#' for proportions.
11		#' @param na_level (`string`)\cr the string you have been using to represent NA or
12		#' missing data. For `NA` values please consider using directly [is.na()] or
13		#' similar approaches.
14		#'
15		#' @return Nothing if assertion passes, otherwise prints the error message.
16		#'
17		#' @name assertions
18		NULL
19
20		check_list_of_variables <- function(x) {
21		# drop NULL elements in list
22	2960x	x <- Filter(Negate(is.null), x)
23
24	2960x	res <- checkmate::check_list(x,
25	2960x	names = "named",
26	2960x	min.len = 1,
27	2960x	any.missing = FALSE,
28	2960x	types = "character"
29		)
30		# no empty strings allowed
31	2960x	if (isTRUE(res)) {
32	2955x	res <- checkmate::check_character(unlist(x), min.chars = 1)
33		}
34	2960x	return(res)
35		}
36		#' @describeIn assertions Checks whether `x` is a valid list of variable names.
37		#' `NULL` elements of the list `x` are dropped with `Filter(Negate(is.null), x)`.
38		#'
39		#' @keywords internal
40		assert_list_of_variables <- checkmate::makeAssertionFunction(check_list_of_variables)
41
42		check_df_with_variables <- function(df, variables, na_level = NULL) {
43	2643x	checkmate::assert_data_frame(df)
44	2641x	assert_list_of_variables(variables)
45
46		# flag for equal variables and column names
47	2639x	err_flag <- all(unlist(variables) %in% colnames(df))
48	2639x	checkmate::assert_flag(err_flag)
49
50	2639x	if (isFALSE(err_flag)) {
51	5x	vars <- setdiff(unlist(variables), colnames(df))
52	5x	return(paste(
53	5x	deparse(substitute(df)),
54	5x	"does not contain all specified variables as column names. Missing from data frame:",
55	5x	paste(vars, collapse = ", ")
56		))
57		}
58		# checking if na_level is present and in which column
59	2634x	if (!is.null(na_level)) {
60	9x	checkmate::assert_string(na_level)
61	9x	res <- unlist(lapply(as.list(df)[unlist(variables)], function(x) any(x == na_level)))
62	9x	if (any(res)) {
63	1x	return(paste0(
64	1x	deparse(substitute(df)), " contains explicit na_level (", na_level,
65	1x	") in the following columns: ", paste0(unlist(variables)[res],
66	1x	collapse = ", "
67		)
68		))
69		}
70		}
71	2633x	return(TRUE)
72		}
73		#' @describeIn assertions Check whether `df` is a data frame with the analysis `variables`.
74		#' Please notice how this produces an error when not all variables are present in the
75		#' data.frame while the opposite is not required.
76		#'
77		#' @keywords internal
78		assert_df_with_variables <- checkmate::makeAssertionFunction(check_df_with_variables)
79
80		check_valid_factor <- function(x,
81		min.levels = 1, # nolint
82		max.levels = NULL, # nolint
83		null.ok = TRUE, # nolint
84		any.missing = TRUE, # nolint
85		n.levels = NULL, # nolint
86		len = NULL) {
87		# checks on levels insertion
88	1113x	checkmate::assert_int(min.levels, lower = 1)
89
90		# main factor check
91	1113x	res <- checkmate::check_factor(x,
92	1113x	min.levels = min.levels,
93	1113x	null.ok = null.ok,
94	1113x	max.levels = max.levels,
95	1113x	any.missing = any.missing,
96	1113x	n.levels = n.levels
97		)
98
99		# no empty strings allowed
100	1113x	if (isTRUE(res)) {
101	1099x	res <- checkmate::check_character(levels(x), min.chars = 1)
102		}
103
104	1113x	return(res)
105		}
106		#' @describeIn assertions Check whether `x` is a valid factor (i.e. has levels and no empty
107		#' string levels). Note that `NULL` and `NA` elements are allowed.
108		#'
109		#' @keywords internal
110		assert_valid_factor <- checkmate::makeAssertionFunction(check_valid_factor)
111
112		check_df_with_factors <- function(df,
113		variables,
114		min.levels = 1, # nolint
115		max.levels = NULL, # nolint
116		any.missing = TRUE, # nolint
117		na_level = NULL) {
118	254x	res <- check_df_with_variables(df, variables, na_level)
119		# checking if all the columns specified by variables are valid factors
120	253x	if (isTRUE(res)) {
121		# searching the data.frame with selected columns (variables) as a list
122	251x	res <- lapply(
123	251x	X = as.list(df)[unlist(variables)],
124	251x	FUN = check_valid_factor,
125	251x	min.levels = min.levels,
126	251x	max.levels = max.levels,
127	251x	any.missing = any.missing
128		)
129	251x	res_lo <- unlist(vapply(res, Negate(isTRUE), logical(1)))
130	251x	if (any(res_lo)) {
131	6x	return(paste0(
132	6x	deparse(substitute(df)), " does not contain only factor variables among:",
133	6x	"\n* Column `", paste0(unlist(variables)[res_lo],
134	6x	"` of the data.frame -> ", res[res_lo],
135	6x	collapse = "\n* "
136		)
137		))
138		} else {
139	245x	res <- TRUE
140		}
141		}
142	247x	return(res)
143		}
144
145		#' @describeIn assertions Check whether `df` is a data frame where the analysis `variables`
146		#' are all factors. Note that the creation of `NA` by direct call of `factor()` will
147		#' trim `NA` levels out of the vector list itself.
148		#'
149		#' @keywords internal
150		assert_df_with_factors <- checkmate::makeAssertionFunction(check_df_with_factors)
151
152		#' @describeIn assertions Check whether `x` is a proportion: number between 0 and 1.
153		#'
154		#' @keywords internal
155		assert_proportion_value <- function(x, include_boundaries = FALSE) {
156	18831x	checkmate::assert_number(x, lower = 0, upper = 1)
157	18819x	checkmate::assert_flag(include_boundaries)
158	18819x	if (isFALSE(include_boundaries)) {
159	12931x	checkmate::assert_true(x > 0)
160	12929x	checkmate::assert_true(x < 1)
161		}
162		}

1		#' Univariate formula special term
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The special term `univariate` indicate that the model should be fitted individually for
6		#' every variable included in univariate.
7		#'
8		#' @param x (`character`)\cr a vector of variable names separated by commas.
9		#'
10		#' @return When used within a model formula, produces univariate models for each variable provided.
11		#'
12		#' @details
13		#' If provided alongside with pairwise specification, the model
14		#' `y ~ ARM + univariate(SEX, AGE, RACE)` lead to the study and comparison of the models
15		#' + `y ~ ARM`
16		#' + `y ~ ARM + SEX`
17		#' + `y ~ ARM + AGE`
18		#' + `y ~ ARM + RACE`
19		#'
20		#' @export
21		univariate <- function(x) {
22	2x	structure(x, varname = deparse(substitute(x)))
23		}
24
25		# Get the right-hand-term of a formula
26		rht <- function(x) {
27	4x	checkmate::assert_formula(x)
28	4x	y <- as.character(rev(x)[[1]])
29	4x	return(y)
30		}
31
32		#' Hazard ratio estimation in interactions
33		#'
34		#' This function estimates the hazard ratios between arms when an interaction variable is given with
35		#' specific values.
36		#'
37		#' @param variable,given (`character(2)`)\cr names of the two variables in the interaction. We seek the estimation of
38		#' the levels of `variable` given the levels of `given`.
39		#' @param lvl_var,lvl_given (`character`)\cr corresponding levels given by [levels()].
40		#' @param mmat (named `numeric`) a vector filled with `0`s used as a template to obtain the design matrix.
41		#' @param coef (`numeric`)\cr vector of estimated coefficients.
42		#' @param vcov (`matrix`)\cr variance-covariance matrix of underlying model.
43		#' @param conf_level (`proportion`)\cr confidence level of estimate intervals.
44		#'
45		#' @details Given the cox regression investigating the effect of Arm (A, B, C; reference A)
46		#' and Sex (F, M; reference Female). The model is abbreviated: y ~ Arm + Sex + Arm x Sex.
47		#' The cox regression estimates the coefficients along with a variance-covariance matrix for:
48		#'
49		#' - b1 (arm b), b2 (arm c)
50		#' - b3 (sex m)
51		#' - b4 (arm b: sex m), b5 (arm c: sex m)
52		#'
53		#' Given that I want an estimation of the Hazard Ratio for arm C/sex M, the estimation
54		#' will be given in reference to arm A/Sex M by exp(b2 + b3 + b5)/ exp(b3) = exp(b2 + b5),
55		#' therefore the interaction coefficient is given by b2 + b5 while the standard error is obtained
56		#' as $1.96 * sqrt(Var b2 + Var b5 + 2 * covariance (b2,b5))$ for a confidence level of 0.95.
57		#'
58		#' @return A list of matrices (one per level of variable) with rows corresponding to the combinations of
59		#' `variable` and `given`, with columns:
60		#' * `coef_hat`: Estimation of the coefficient.
61		#' * `coef_se`: Standard error of the estimation.
62		#' * `hr`: Hazard ratio.
63		#' * `lcl, ucl`: Lower/upper confidence limit of the hazard ratio.
64		#'
65		#' @seealso [s_cox_multivariate()].
66		#'
67		#' @examples
68		#' library(dplyr)
69		#' library(survival)
70		#'
71		#' ADSL <- tern_ex_adsl %>%
72		#' filter(SEX %in% c("F", "M"))
73		#'
74		#' adtte <- tern_ex_adtte %>% filter(PARAMCD == "PFS")
75		#' adtte$ARMCD <- droplevels(adtte$ARMCD)
76		#' adtte$SEX <- droplevels(adtte$SEX)
77		#'
78		#' mod <- coxph(
79		#' formula = Surv(time = AVAL, event = 1 - CNSR) ~ (SEX + ARMCD)^2,
80		#' data = adtte
81		#' )
82		#'
83		#' mmat <- stats::model.matrix(mod)[1, ]
84		#' mmat[!mmat == 0] <- 0
85		#'
86		#' @keywords internal
87		estimate_coef <- function(variable, given,
88		lvl_var, lvl_given,
89		coef,
90		mmat,
91		vcov,
92		conf_level = 0.95) {
93	8x	var_lvl <- paste0(variable, lvl_var[-1]) # [-1]: reference level
94	8x	giv_lvl <- paste0(given, lvl_given)
95
96	8x	design_mat <- expand.grid(variable = var_lvl, given = giv_lvl)
97	8x	design_mat <- design_mat[order(design_mat$variable, design_mat$given), ]
98	8x	design_mat <- within(
99	8x	data = design_mat,
100	8x	expr = {
101	8x	inter <- paste0(variable, ":", given)
102	8x	rev_inter <- paste0(given, ":", variable)
103		}
104		)
105
106	8x	split_by_variable <- design_mat$variable
107	8x	interaction_names <- paste(design_mat$variable, design_mat$given, sep = "/")
108
109	8x	design_mat <- apply(
110	8x	X = design_mat, MARGIN = 1, FUN = function(x) {
111	27x	mmat[names(mmat) %in% x[-which(names(x) == "given")]] <- 1
112	27x	return(mmat)
113		}
114		)
115	8x	colnames(design_mat) <- interaction_names
116
117	8x	betas <- as.matrix(coef)
118
119	8x	coef_hat <- t(design_mat) %*% betas
120	8x	dimnames(coef_hat)[2] <- "coef"
121
122	8x	coef_se <- apply(design_mat, 2, function(x) {
123	27x	vcov_el <- as.logical(x)
124	27x	y <- vcov[vcov_el, vcov_el]
125	27x	y <- sum(y)
126	27x	y <- sqrt(y)
127	27x	return(y)
128		})
129
130	8x	q_norm <- stats::qnorm((1 + conf_level) / 2)
131	8x	y <- cbind(coef_hat, `se(coef)` = coef_se)
132
133	8x	y <- apply(y, 1, function(x) {
134	27x	x["hr"] <- exp(x["coef"])
135	27x	x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
136	27x	x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])
137
138	27x	return(x)
139		})
140
141	8x	y <- t(y)
142	8x	y <- by(y, split_by_variable, identity)
143	8x	y <- lapply(y, as.matrix)
144
145	8x	attr(y, "details") <- paste0(
146	8x	"Estimations of ", variable,
147	8x	" hazard ratio given the level of ", given, " compared to ",
148	8x	variable, " level ", lvl_var[1], "."
149		)
150	8x	return(y)
151		}
152
153		#' `tryCatch` around `car::Anova`
154		#'
155		#' Captures warnings when executing [car::Anova].
156		#'
157		#' @inheritParams car::Anova
158		#'
159		#' @return A list with item `aov` for the result of the model and `error_text` for the captured warnings.
160		#'
161		#' @examples
162		#' # `car::Anova` on cox regression model including strata and expected
163		#' # a likelihood ratio test triggers a warning as only Wald method is
164		#' # accepted.
165		#'
166		#' library(survival)
167		#'
168		#' mod <- coxph(
169		#' formula = Surv(time = futime, event = fustat) ~ factor(rx) + strata(ecog.ps),
170		#' data = ovarian
171		#' )
172		#'
173		#' @keywords internal
174		try_car_anova <- function(mod,
175		test.statistic) { # nolint
176	1x	y <- tryCatch(
177	1x	withCallingHandlers(
178	1x	expr = {
179	1x	warn_text <- c()
180	1x	list(
181	1x	aov = car::Anova(
182	1x	mod,
183	1x	test.statistic = test.statistic,
184	1x	type = "III"
185		),
186	1x	warn_text = warn_text
187		)
188		},
189	1x	warning = function(w) {
190		# If a warning is detected it is handled as "w".
191	!	warn_text <<- trimws(paste0("Warning in `try_car_anova`: ", w))
192
193		# A warning is sometimes expected, then, we want to restart
194		# the execution while ignoring the warning.
195	!	invokeRestart("muffleWarning")
196		}
197		),
198	1x	finally = {
199		}
200		)
201
202	1x	return(y)
203		}
204
205		#' Fit a Cox regression model and ANOVA
206		#'
207		#' The functions derives the effect p-values using [car::Anova()] from [survival::coxph()] results.
208		#'
209		#' @inheritParams t_coxreg
210		#'
211		#' @return A list with items `mod` (results of [survival::coxph()]), `msum` (result of `summary`) and
212		#' `aov` (result of [car::Anova()]).
213		#'
214		#' @noRd
215		fit_n_aov <- function(formula,
216		data = data,
217		conf_level = conf_level,
218		pval_method = c("wald", "likelihood"),
219		...) {
220	1x	pval_method <- match.arg(pval_method)
221
222	1x	environment(formula) <- environment()
223	1x	suppressWarnings({
224		# We expect some warnings due to coxph which fails strict programming.
225	1x	mod <- survival::coxph(formula, data = data, ...)
226	1x	msum <- summary(mod, conf.int = conf_level)
227		})
228
229	1x	aov <- try_car_anova(
230	1x	mod,
231	1x	test.statistic = switch(pval_method,
232	1x	"wald" = "Wald",
233	1x	"likelihood" = "LR"
234		)
235		)
236
237	1x	warn_attr <- aov$warn_text
238	!	if (!is.null(aov$warn_text)) message(warn_attr)
239
240	1x	aov <- aov$aov
241	1x	y <- list(mod = mod, msum = msum, aov = aov)
242	1x	attr(y, "message") <- warn_attr
243
244	1x	return(y)
245		}
246
247		# argument_checks
248		check_formula <- function(formula) {
249	1x	if (!(inherits(formula, "formula"))) {
250	1x	stop("Check `formula`. A formula should resemble `Surv(time = AVAL, event = 1 - CNSR) ~ study_arm(ARMCD)`.")
251		}
252
253	!	invisible()
254		}
255
256		check_covariate_formulas <- function(covariates) {
257	1x	if (!all(vapply(X = covariates, FUN = inherits, what = "formula", FUN.VALUE = TRUE)) \|\| is.null(covariates)) {
258	1x	stop("Check `covariates`, it should be a list of right-hand-term formulas, e.g. list(Age = ~AGE).")
259		}
260
261	!	invisible()
262		}
263
264		name_covariate_names <- function(covariates) {
265	1x	miss_names <- names(covariates) == ""
266	1x	no_names <- is.null(names(covariates))
267	!	if (any(miss_names)) names(covariates)[miss_names] <- vapply(covariates[miss_names], FUN = rht, FUN.VALUE = "name")
268	!	if (no_names) names(covariates) <- vapply(covariates, FUN = rht, FUN.VALUE = "name")
269	1x	return(covariates)
270		}
271
272		check_increments <- function(increments, covariates) {
273	1x	if (!is.null(increments)) {
274	1x	covariates <- vapply(covariates, FUN = rht, FUN.VALUE = "name")
275	1x	lapply(
276	1x	X = names(increments), FUN = function(x) {
277	3x	if (!x %in% covariates) {
278	1x	warning(
279	1x	paste(
280	1x	"Check `increments`, the `increment` for ", x,
281	1x	"doesn't match any names in investigated covariate(s)."
282		)
283		)
284		}
285		}
286		)
287		}
288
289	1x	invisible()
290		}
291
292		#' Multivariate Cox model - summarized results
293		#'
294		#' Analyses based on multivariate Cox model are usually not performed for the Controlled Substance Reporting or
295		#' regulatory documents but serve exploratory purposes only (e.g., for publication). In practice, the model usually
296		#' includes only the main effects (without interaction terms). It produces the hazard ratio estimates for each of the
297		#' covariates included in the model.
298		#' The analysis follows the same principles (e.g., stratified vs. unstratified analysis and tie handling) as the
299		#' usual Cox model analysis. Since there is usually no pre-specified hypothesis testing for such analysis,
300		#' the p.values need to be interpreted with caution. (Statistical Analysis of Clinical Trials Data with R,
301		#' `NEST's bookdown`)
302		#'
303		#' @param formula (`formula`)\cr a formula corresponding to the investigated [survival::Surv()] survival model
304		#' including covariates.
305		#' @param data (`data.frame`)\cr a data frame which includes the variable in formula and covariates.
306		#' @param conf_level (`proportion`)\cr the confidence level for the hazard ratio interval estimations. Default is 0.95.
307		#' @param pval_method (`string`)\cr the method used for the estimation of p-values, should be one of
308		#' `"wald"` (default) or `"likelihood"`.
309		#' @param ... optional parameters passed to [survival::coxph()]. Can include `ties`, a character string specifying the
310		#' method for tie handling, one of `exact` (default), `efron`, `breslow`.
311		#'
312		#' @return A `list` with elements `mod`, `msum`, `aov`, and `coef_inter`.
313		#'
314		#' @details The output is limited to single effect terms. Work in ongoing for estimation of interaction terms
315		#' but is out of scope as defined by the Global Data Standards Repository
316		#' (`GDS_Standard_TLG_Specs_Tables_2.doc`).
317		#'
318		#' @seealso [estimate_coef()].
319		#'
320		#' @examples
321		#' library(dplyr)
322		#'
323		#' adtte <- tern_ex_adtte
324		#' adtte_f <- subset(adtte, PARAMCD == "OS") # _f: filtered
325		#' adtte_f <- filter(
326		#' adtte_f,
327		#' PARAMCD == "OS" &
328		#' SEX %in% c("F", "M") &
329		#' RACE %in% c("ASIAN", "BLACK OR AFRICAN AMERICAN", "WHITE")
330		#' )
331		#' adtte_f$SEX <- droplevels(adtte_f$SEX)
332		#' adtte_f$RACE <- droplevels(adtte_f$RACE)
333		#'
334		#' @keywords internal
335		s_cox_multivariate <- function(formula, data,
336		conf_level = 0.95,
337		pval_method = c("wald", "likelihood"),
338		...) {
339	1x	tf <- stats::terms(formula, specials = c("strata"))
340	1x	covariates <- rownames(attr(tf, "factors"))[-c(1, unlist(attr(tf, "specials")))]
341	1x	lapply(
342	1x	X = covariates,
343	1x	FUN = function(x) {
344	3x	if (is.character(data[[x]])) {
345	1x	data[[x]] <<- as.factor(data[[x]])
346		}
347	3x	invisible()
348		}
349		)
350	1x	pval_method <- match.arg(pval_method)
351
352		# Results directly exported from environment(fit_n_aov) to environment(s_function_draft)
353	1x	y <- fit_n_aov(
354	1x	formula = formula,
355	1x	data = data,
356	1x	conf_level = conf_level,
357	1x	pval_method = pval_method,
358		...
359		)
360	1x	mod <- y$mod
361	1x	aov <- y$aov
362	1x	msum <- y$msum
363	1x	list2env(as.list(y), environment())
364
365	1x	all_term_labs <- attr(mod$terms, "term.labels")
366	1x	term_labs <- all_term_labs[which(attr(mod$terms, "order") == 1)]
367	1x	names(term_labs) <- term_labs
368
369	1x	coef_inter <- NULL
370	1x	if (any(attr(mod$terms, "order") > 1)) {
371	1x	for_inter <- all_term_labs[attr(mod$terms, "order") > 1]
372	1x	names(for_inter) <- for_inter
373	1x	mmat <- stats::model.matrix(mod)[1, ]
374	1x	mmat[!mmat == 0] <- 0
375	1x	mcoef <- stats::coef(mod)
376	1x	mvcov <- stats::vcov(mod)
377
378	1x	estimate_coef_local <- function(variable, given) {
379	6x	estimate_coef(
380	6x	variable, given,
381	6x	coef = mcoef, mmat = mmat, vcov = mvcov, conf_level = conf_level,
382	6x	lvl_var = levels(data[[variable]]), lvl_given = levels(data[[given]])
383		)
384		}
385
386	1x	coef_inter <- lapply(
387	1x	for_inter, function(x) {
388	3x	y <- attr(mod$terms, "factors")[, x]
389	3x	y <- names(y[y > 0])
390	3x	Map(estimate_coef_local, variable = y, given = rev(y))
391		}
392		)
393		}
394
395	1x	list(mod = mod, msum = msum, aov = aov, coef_inter = coef_inter)
396		}

1		#' Count number of patients
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [analyze_num_patients()] creates a layout element to count total numbers of unique or
6		#' non-unique patients. The primary analysis variable `vars` is used to uniquely identify patients.
7		#'
8		#' The `count_by` variable can be used to identify non-unique patients such that the number of patients with a unique
9		#' combination of values in `vars` and `count_by` will be returned instead as the `nonunique` statistic. The `required`
10		#' variable can be used to specify a variable required to be non-missing for the record to be included in the counts.
11		#'
12		#' The summarize function [summarize_num_patients()] performs the same function as [analyze_num_patients()] except it
13		#' creates content rows, not data rows, to summarize the current table row/column context and operates on the level of
14		#' the latest row split or the root of the table if no row splits have occurred.
15		#'
16		#' @inheritParams argument_convention
17		#' @param required (`character` or `NULL`)\cr name of a variable that is required to be non-missing.
18		#' @param count_by (`character` or `NULL`)\cr name of a variable to be combined with `vars` when counting
19		#' `nonunique` records.
20		#' @param unique_count_suffix (`flag`)\cr whether the `"(n)"` suffix should be added to `unique_count` labels.
21		#' Defaults to `TRUE`.
22		#' @param .stats (`character`)\cr statistics to select for the table.
23		#'
24		#' Options are: ``r shQuote(get_stats("summarize_num_patients"), type = "sh")``
25		#'
26		#' @name summarize_num_patients
27		#' @order 1
28		NULL
29
30		#' @describeIn summarize_num_patients Statistics function which counts the number of
31		#' unique patients, the corresponding percentage taken with respect to the
32		#' total number of patients, and the number of non-unique patients.
33		#'
34		#' @param x (`character` or `factor`)\cr vector of patient IDs.
35		#'
36		#' @return
37		#' * `s_num_patients()` returns a named `list` of 3 statistics:
38		#' * `unique`: Vector of counts and percentages.
39		#' * `nonunique`: Vector of counts.
40		#' * `unique_count`: Counts.
41		#'
42		#' @examples
43		#' # Use the statistics function to count number of unique and nonunique patients.
44		#' s_num_patients(x = as.character(c(1, 1, 1, 2, 4, NA)), labelstr = "", .N_col = 6L)
45		#' s_num_patients(
46		#' x = as.character(c(1, 1, 1, 2, 4, NA)),
47		#' labelstr = "",
48		#' .N_col = 6L,
49		#' count_by = c(1, 1, 2, 1, 1, 1)
50		#' )
51		#'
52		#' @export
53		s_num_patients <- function(x, labelstr, .N_col, count_by = NULL, unique_count_suffix = TRUE) { # nolint
54
55	146x	checkmate::assert_string(labelstr)
56	146x	checkmate::assert_count(.N_col)
57	146x	checkmate::assert_multi_class(x, classes = c("factor", "character"))
58	146x	checkmate::assert_flag(unique_count_suffix)
59
60	146x	count1 <- n_available(unique(x))
61	146x	count2 <- n_available(x)
62
63	146x	if (!is.null(count_by)) {
64	16x	checkmate::assert_vector(count_by, len = length(x))
65	16x	count2 <- n_available(unique(interaction(x, count_by)))
66		}
67
68	146x	out <- list(
69	146x	unique = formatters::with_label(c(count1, ifelse(count1 == 0 && .N_col == 0, 0, count1 / .N_col)), labelstr),
70	146x	nonunique = formatters::with_label(count2, labelstr),
71	146x	unique_count = formatters::with_label(
72	146x	count1, ifelse(unique_count_suffix, paste0(labelstr, if (nzchar(labelstr)) " ", "(n)"), labelstr)
73		)
74		)
75
76	146x	out
77		}
78
79		#' @describeIn summarize_num_patients Statistics function which counts the number of unique patients
80		#' in a column (variable), the corresponding percentage taken with respect to the total number of
81		#' patients, and the number of non-unique patients in the column.
82		#'
83		#' @return
84		#' * `s_num_patients_content()` returns the same values as `s_num_patients()`.
85		#'
86		#' @examples
87		#' # Count number of unique and non-unique patients.
88		#'
89		#' df <- data.frame(
90		#' USUBJID = as.character(c(1, 2, 1, 4, NA)),
91		#' EVENT = as.character(c(10, 15, 10, 17, 8))
92		#' )
93		#' s_num_patients_content(df, .N_col = 5, .var = "USUBJID")
94		#'
95		#' df_by_event <- data.frame(
96		#' USUBJID = as.character(c(1, 2, 1, 4, NA)),
97		#' EVENT = c(10, 15, 10, 17, 8)
98		#' )
99		#' s_num_patients_content(df_by_event, .N_col = 5, .var = "USUBJID", count_by = "EVENT")
100		#'
101		#' @export
102		s_num_patients_content <- function(df,
103		labelstr = "",
104		.N_col, # nolint
105		.var,
106		required = NULL,
107		count_by = NULL,
108		unique_count_suffix = TRUE) {
109	56x	checkmate::assert_string(.var)
110	56x	checkmate::assert_data_frame(df)
111	56x	if (is.null(count_by)) {
112	53x	assert_df_with_variables(df, list(id = .var))
113		} else {
114	3x	assert_df_with_variables(df, list(id = .var, count_by = count_by))
115		}
116	56x	if (!is.null(required)) {
117	!	checkmate::assert_string(required)
118	!	assert_df_with_variables(df, list(required = required))
119	!	df <- df[!is.na(df[[required]]), , drop = FALSE]
120		}
121
122	56x	x <- df[[.var]]
123	56x	y <- if (is.null(count_by)) NULL else df[[count_by]]
124
125	56x	s_num_patients(
126	56x	x = x,
127	56x	labelstr = labelstr,
128	56x	.N_col = .N_col,
129	56x	count_by = y,
130	56x	unique_count_suffix = unique_count_suffix
131		)
132		}
133
134		c_num_patients <- make_afun(
135		s_num_patients_content,
136		.stats = c("unique", "nonunique", "unique_count"),
137		.formats = c(unique = format_count_fraction_fixed_dp, nonunique = "xx", unique_count = "xx")
138		)
139
140		#' @describeIn summarize_num_patients Layout-creating function which can take statistics function arguments
141		#' and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
142		#'
143		#' @return
144		#' * `summarize_num_patients()` returns a layout object suitable for passing to further layouting functions,
145		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
146		#' the statistics from `s_num_patients_content()` to the table layout.
147		#'
148		#' @examples
149		#' # summarize_num_patients
150		#' tbl <- basic_table() %>%
151		#' split_cols_by("ARM") %>%
152		#' split_rows_by("SEX") %>%
153		#' summarize_num_patients("USUBJID", .stats = "unique_count") %>%
154		#' build_table(df)
155		#'
156		#' tbl
157		#'
158		#' @export
159		#' @order 3
160		summarize_num_patients <- function(lyt,
161		var,
162		required = NULL,
163		count_by = NULL,
164		unique_count_suffix = TRUE,
165		na_str = default_na_str(),
166		.stats = NULL,
167		.formats = NULL,
168		.labels = c(
169		unique = "Number of patients with at least one event",
170		nonunique = "Number of events"
171		),
172		.indent_mods = 0L,
173		riskdiff = FALSE,
174		...) {
175	16x	checkmate::assert_flag(riskdiff)
176
177	5x	if (is.null(.stats)) .stats <- c("unique", "nonunique", "unique_count")
178	8x	if (length(.labels) > length(.stats)) .labels <- .labels[names(.labels) %in% .stats]
179
180	16x	s_args <- list(required = required, count_by = count_by, unique_count_suffix = unique_count_suffix, ...)
181
182	16x	cfun <- make_afun(
183	16x	c_num_patients,
184	16x	.stats = .stats,
185	16x	.formats = .formats,
186	16x	.labels = .labels
187		)
188
189	16x	extra_args <- if (isFALSE(riskdiff)) {
190	14x	s_args
191		} else {
192	2x	list(
193	2x	afun = list("s_num_patients_content" = cfun),
194	2x	.stats = .stats,
195	2x	.indent_mods = .indent_mods,
196	2x	s_args = s_args
197		)
198		}
199
200	16x	summarize_row_groups(
201	16x	lyt = lyt,
202	16x	var = var,
203	16x	cfun = ifelse(isFALSE(riskdiff), cfun, afun_riskdiff),
204	16x	na_str = na_str,
205	16x	extra_args = extra_args,
206	16x	indent_mod = .indent_mods
207		)
208		}
209
210		#' @describeIn summarize_num_patients Layout-creating function which can take statistics function arguments
211		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
212		#'
213		#' @return
214		#' * `analyze_num_patients()` returns a layout object suitable for passing to further layouting functions,
215		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
216		#' the statistics from `s_num_patients_content()` to the table layout.
217		#'
218		#' @details In general, functions that starts with `analyze*` are expected to
219		#' work like [rtables::analyze()], while functions that starts with `summarize*`
220		#' are based upon [rtables::summarize_row_groups()]. The latter provides a
221		#' value for each dividing split in the row and column space, but, being it
222		#' bound to the fundamental splits, it is repeated by design in every page
223		#' when pagination is involved.
224		#'
225		#' @note As opposed to [summarize_num_patients()], this function does not repeat the produced rows.
226		#'
227		#' @examples
228		#' df <- data.frame(
229		#' USUBJID = as.character(c(1, 2, 1, 4, NA, 6, 6, 8, 9)),
230		#' ARM = c("A", "A", "A", "A", "A", "B", "B", "B", "B"),
231		#' AGE = c(10, 15, 10, 17, 8, 11, 11, 19, 17),
232		#' SEX = c("M", "M", "M", "F", "F", "F", "M", "F", "M")
233		#' )
234		#'
235		#' # analyze_num_patients
236		#' tbl <- basic_table() %>%
237		#' split_cols_by("ARM") %>%
238		#' add_colcounts() %>%
239		#' analyze_num_patients("USUBJID", .stats = c("unique")) %>%
240		#' build_table(df)
241		#'
242		#' tbl
243		#'
244		#' @export
245		#' @order 2
246		analyze_num_patients <- function(lyt,
247		vars,
248		required = NULL,
249		count_by = NULL,
250		unique_count_suffix = TRUE,
251		na_str = default_na_str(),
252		nested = TRUE,
253		.stats = NULL,
254		.formats = NULL,
255		.labels = c(
256		unique = "Number of patients with at least one event",
257		nonunique = "Number of events"
258		),
259		show_labels = c("default", "visible", "hidden"),
260		.indent_mods = 0L,
261		riskdiff = FALSE,
262		...) {
263	4x	checkmate::assert_flag(riskdiff)
264
265	1x	if (is.null(.stats)) .stats <- c("unique", "nonunique", "unique_count")
266	!	if (length(.labels) > length(.stats)) .labels <- .labels[names(.labels) %in% .stats]
267
268	4x	s_args <- list(required = required, count_by = count_by, unique_count_suffix = unique_count_suffix, ...)
269
270	4x	afun <- make_afun(
271	4x	c_num_patients,
272	4x	.stats = .stats,
273	4x	.formats = .formats,
274	4x	.labels = .labels
275		)
276
277	4x	extra_args <- if (isFALSE(riskdiff)) {
278	2x	s_args
279		} else {
280	2x	list(
281	2x	afun = list("s_num_patients_content" = afun),
282	2x	.stats = .stats,
283	2x	.indent_mods = .indent_mods,
284	2x	s_args = s_args
285		)
286		}
287
288	4x	analyze(
289	4x	afun = ifelse(isFALSE(riskdiff), afun, afun_riskdiff),
290	4x	lyt = lyt,
291	4x	vars = vars,
292	4x	na_str = na_str,
293	4x	nested = nested,
294	4x	extra_args = extra_args,
295	4x	show_labels = show_labels,
296	4x	indent_mod = .indent_mods
297		)
298		}

1		#' Count specific values
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [count_values()] creates a layout element to calculate counts of specific values within a
6		#' variable of interest.
7		#'
8		#' This function analyzes one or more variables of interest supplied as a vector to `vars`. Values to
9		#' count for variable(s) in `vars` can be given as a vector via the `values` argument. One row of
10		#' counts will be generated for each variable.
11		#'
12		#' @inheritParams argument_convention
13		#' @param values (`character`)\cr specific values that should be counted.
14		#' @param .stats (`character`)\cr statistics to select for the table.
15		#'
16		#' Options are: ``r shQuote(get_stats("count_values"), type = "sh")``
17		#'
18		#' @note
19		#' * For `factor` variables, `s_count_values` checks whether `values` are all included in the levels of `x`
20		#' and fails otherwise.
21		#' * For `count_values()`, variable labels are shown when there is more than one element in `vars`,
22		#' otherwise they are hidden.
23		#'
24		#' @name count_values
25		#' @order 1
26		NULL
27
28		#' @describeIn count_values S3 generic function to count values.
29		#'
30		#' @inheritParams s_summary.logical
31		#'
32		#' @return
33		#' * `s_count_values()` returns output of [s_summary()] for specified values of a non-numeric variable.
34		#'
35		#' @export
36		s_count_values <- function(x,
37		values,
38		na.rm = TRUE, # nolint
39		denom = c("n", "N_col", "N_row"),
40		...) {
41	207x	UseMethod("s_count_values", x)
42		}
43
44		#' @describeIn count_values Method for `character` class.
45		#'
46		#' @method s_count_values character
47		#'
48		#' @examples
49		#' # `s_count_values.character`
50		#' s_count_values(x = c("a", "b", "a"), values = "a")
51		#' s_count_values(x = c("a", "b", "a", NA, NA), values = "b", na.rm = FALSE)
52		#'
53		#' @export
54		s_count_values.character <- function(x,
55		values = "Y",
56		na.rm = TRUE, # nolint
57		...) {
58	200x	checkmate::assert_character(values)
59
60	200x	if (na.rm) {
61	199x	x <- x[!is.na(x)]
62		}
63
64	200x	is_in_values <- x %in% values
65
66	200x	s_summary(is_in_values, na_rm = na.rm, ...)
67		}
68
69		#' @describeIn count_values Method for `factor` class. This makes an automatic
70		#' conversion to `character` and then forwards to the method for characters.
71		#'
72		#' @method s_count_values factor
73		#'
74		#' @examples
75		#' # `s_count_values.factor`
76		#' s_count_values(x = factor(c("a", "b", "a")), values = "a")
77		#'
78		#' @export
79		s_count_values.factor <- function(x,
80		values = "Y",
81		...) {
82	4x	s_count_values(as.character(x), values = as.character(values), ...)
83		}
84
85		#' @describeIn count_values Method for `logical` class.
86		#'
87		#' @method s_count_values logical
88		#'
89		#' @examples
90		#' # `s_count_values.logical`
91		#' s_count_values(x = c(TRUE, FALSE, TRUE))
92		#'
93		#' @export
94		s_count_values.logical <- function(x, values = TRUE, ...) {
95	3x	checkmate::assert_logical(values)
96	3x	s_count_values(as.character(x), values = as.character(values), ...)
97		}
98
99		#' @describeIn count_values Formatted analysis function which is used as `afun`
100		#' in `count_values()`.
101		#'
102		#' @return
103		#' * `a_count_values()` returns the corresponding list with formatted [rtables::CellValue()].
104		#'
105		#' @examples
106		#' # `a_count_values`
107		#' a_count_values(x = factor(c("a", "b", "a")), values = "a", .N_col = 10, .N_row = 10)
108		#'
109		#' @export
110		a_count_values <- function(x,
111		...,
112		.stats = NULL,
113		.stat_names = NULL,
114		.formats = NULL,
115		.labels = NULL,
116		.indent_mods = NULL) {
117	17x	dots_extra_args <- list(...)
118
119		# Check for user-defined functions
120	17x	default_and_custom_stats_list <- .split_std_from_custom_stats(.stats)
121	17x	.stats <- default_and_custom_stats_list$default_stats
122	17x	custom_stat_functions <- default_and_custom_stats_list$custom_stats
123
124		# Add extra parameters to the s_* function
125	17x	extra_afun_params <- retrieve_extra_afun_params(
126	17x	names(dots_extra_args$.additional_fun_parameters)
127		)
128	17x	dots_extra_args$.additional_fun_parameters <- NULL
129
130		# Main statistic calculations
131	17x	x_stats <- .apply_stat_functions(
132	17x	default_stat_fnc = s_count_values,
133	17x	custom_stat_fnc_list = custom_stat_functions,
134	17x	args_list = c(
135	17x	x = list(x),
136	17x	extra_afun_params,
137	17x	dots_extra_args
138		)
139		)
140
141		# Fill in formatting defaults
142	17x	.stats <- c(
143	17x	get_stats("analyze_vars_counts", stats_in = .stats),
144	17x	names(custom_stat_functions) # Additional stats from custom functions
145		)
146	17x	.formats <- get_formats_from_stats(.stats, .formats)
147	17x	.labels <- get_labels_from_stats(.stats, .labels)
148	17x	.indent_mods <- get_indents_from_stats(.stats, .indent_mods)
149
150	17x	x_stats <- x_stats[.stats]
151
152		# Auto format handling
153	17x	.formats <- apply_auto_formatting(
154	17x	.formats,
155	17x	x_stats,
156	17x	extra_afun_params$.df_row,
157	17x	extra_afun_params$.var
158		)
159
160		# Get and check statistic names from defaults
161	17x	.stat_names <- get_stat_names(x_stats, .stat_names)
162
163	17x	in_rows(
164	17x	.list = x_stats,
165	17x	.formats = .formats,
166	17x	.names = names(.labels),
167	17x	.stat_names = .stat_names,
168	17x	.labels = .labels %>% .unlist_keep_nulls(),
169	17x	.indent_mods = .indent_mods %>% .unlist_keep_nulls()
170		)
171		}
172
173		#' @describeIn count_values Layout-creating function which can take statistics function arguments
174		#' and additional format arguments. This function is a wrapper for [rtables::analyze()].
175		#'
176		#' @return
177		#' * `count_values()` returns a layout object suitable for passing to further layouting functions,
178		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
179		#' the statistics from `s_count_values()` to the table layout.
180		#'
181		#' @examples
182		#' # `count_values`
183		#' basic_table() %>%
184		#' count_values("Species", values = "setosa") %>%
185		#' build_table(iris)
186		#'
187		#' @export
188		#' @order 2
189		count_values <- function(lyt,
190		vars,
191		values,
192		na_str = default_na_str(),
193		na_rm = TRUE,
194		nested = TRUE,
195		...,
196		table_names = vars,
197		.stats = "count_fraction",
198		.stat_names = NULL,
199		.formats = c(count_fraction = "xx (xx.xx%)", count = "xx"),
200		.labels = c(count_fraction = paste(values, collapse = ", ")),
201		.indent_mods = NULL) {
202		# Process extra args
203	8x	extra_args <- list("na_rm" = na_rm, "values" = values, ...)
204	8x	if (!is.null(.stats)) extra_args[[".stats"]] <- .stats
205	!	if (!is.null(.stat_names)) extra_args[[".stat_names"]] <- .stat_names
206	8x	if (!is.null(.formats)) extra_args[[".formats"]] <- .formats
207	8x	if (!is.null(.labels)) extra_args[[".labels"]] <- .labels
208	!	if (!is.null(.indent_mods)) extra_args[[".indent_mods"]] <- .indent_mods
209
210		# Adding additional info from layout to analysis function
211	8x	extra_args[[".additional_fun_parameters"]] <- get_additional_afun_params(add_alt_df = FALSE)
212	8x	formals(a_count_values) <- c(formals(a_count_values), extra_args[[".additional_fun_parameters"]])
213
214	8x	analyze(
215	8x	lyt,
216	8x	vars,
217	8x	afun = a_count_values,
218	8x	na_str = na_str,
219	8x	nested = nested,
220	8x	extra_args = extra_args,
221	8x	show_labels = ifelse(length(vars) > 1, "visible", "hidden"),
222	8x	table_names = table_names
223		)
224		}

1		#' Count occurrences by grade
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The analyze function [count_occurrences_by_grade()] creates a layout element to calculate occurrence counts by grade.
6		#'
7		#' This function analyzes primary analysis variable `var` which indicates toxicity grades. The `id` variable
8		#' is used to indicate unique subject identifiers (defaults to `USUBJID`). The user can also supply a list of
9		#' custom groups of grades to analyze via the `grade_groups` parameter. The `remove_single` argument will
10		#' remove single grades from the analysis so that only grade groups are analyzed.
11		#'
12		#' If there are multiple grades recorded for one patient only the highest grade level is counted.
13		#'
14		#' The summarize function [summarize_occurrences_by_grade()] performs the same function as
15		#' [count_occurrences_by_grade()] except it creates content rows, not data rows, to summarize the current table
16		#' row/column context and operates on the level of the latest row split or the root of the table if no row splits have
17		#' occurred.
18		#'
19		#' @inheritParams count_occurrences
20		#' @inheritParams argument_convention
21		#' @param grade_groups (named `list` of `character`)\cr list containing groupings of grades.
22		#' @param remove_single (`flag`)\cr `TRUE` to not include the elements of one-element grade groups
23		#' in the the output list; in this case only the grade groups names will be included in the output. If
24		#' `only_grade_groups` is set to `TRUE` this argument is ignored.
25		#' @param only_grade_groups (`flag`)\cr whether only the specified grade groups should be
26		#' included, with individual grade rows removed (`TRUE`), or all grades and grade groups
27		#' should be displayed (`FALSE`).
28		#' @param .stats (`character`)\cr statistics to select for the table.
29		#'
30		#' Options are: ``r shQuote(get_stats("count_occurrences_by_grade"), type = "sh")``
31		#'
32		#' @seealso Relevant helper function [h_append_grade_groups()].
33		#'
34		#' @name count_occurrences_by_grade
35		#' @order 1
36		NULL
37
38		#' Helper function for `s_count_occurrences_by_grade()`
39		#'
40		#' @description `r lifecycle::badge("stable")`
41		#'
42		#' Helper function for [s_count_occurrences_by_grade()] to insert grade groupings into list with
43		#' individual grade frequencies. The order of the final result follows the order of `grade_groups`.
44		#' The elements under any-grade group (if any), i.e. the grade group equal to `refs` will be moved to
45		#' the end. Grade groups names must be unique.
46		#'
47		#' @inheritParams count_occurrences_by_grade
48		#' @param refs (named `list` of `numeric`)\cr named list where each name corresponds to a reference grade level
49		#' and each entry represents a count.
50		#'
51		#' @return Formatted list of grade groupings.
52		#'
53		#' @examples
54		#' h_append_grade_groups(
55		#' list(
56		#' "Any Grade" = as.character(1:5),
57		#' "Grade 1-2" = c("1", "2"),
58		#' "Grade 3-4" = c("3", "4")
59		#' ),
60		#' list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)
61		#' )
62		#'
63		#' h_append_grade_groups(
64		#' list(
65		#' "Any Grade" = as.character(5:1),
66		#' "Grade A" = "5",
67		#' "Grade B" = c("4", "3")
68		#' ),
69		#' list("1" = 10, "2" = 20, "3" = 30, "4" = 40, "5" = 50)
70		#' )
71		#'
72		#' h_append_grade_groups(
73		#' list(
74		#' "Any Grade" = as.character(1:5),
75		#' "Grade 1-2" = c("1", "2"),
76		#' "Grade 3-4" = c("3", "4")
77		#' ),
78		#' list("1" = 10, "2" = 5, "3" = 0)
79		#' )
80		#'
81		#' @export
82		h_append_grade_groups <- function(grade_groups, refs, remove_single = TRUE, only_grade_groups = FALSE) {
83	32x	checkmate::assert_list(grade_groups)
84	32x	checkmate::assert_list(refs)
85	32x	refs_orig <- refs
86	32x	elements <- unique(unlist(grade_groups))
87
88		### compute sums in groups
89	32x	grp_sum <- lapply(grade_groups, function(i) do.call(sum, refs[i]))
90	32x	if (!checkmate::test_subset(elements, names(refs))) {
91	2x	padding_el <- setdiff(elements, names(refs))
92	2x	refs[padding_el] <- 0
93		}
94	32x	result <- c(grp_sum, refs)
95
96		### order result while keeping grade_groups's ordering
97	32x	ordr <- grade_groups
98
99		# elements of any-grade group (if any) will be moved to the end
100	32x	is_any <- sapply(grade_groups, setequal, y = names(refs))
101	32x	ordr[is_any] <- list(character(0)) # hide elements under any-grade group
102
103		# groups-elements combined sequence
104	32x	ordr <- c(lapply(names(ordr), function(g) c(g, ordr[[g]])), recursive = TRUE, use.names = FALSE)
105	32x	ordr <- ordr[!duplicated(ordr)]
106
107		# append remaining elements (if any)
108	32x	ordr <- union(ordr, unlist(grade_groups[is_any])) # from any-grade group
109	32x	ordr <- union(ordr, names(refs)) # from refs
110
111		# remove elements of single-element groups, if any
112	32x	if (only_grade_groups) {
113	3x	ordr <- intersect(ordr, names(grade_groups))
114	29x	} else if (remove_single) {
115	29x	is_single <- sapply(grade_groups, length) == 1L
116	29x	ordr <- setdiff(ordr, unlist(grade_groups[is_single]))
117		}
118
119		# apply the order
120	32x	result <- result[ordr]
121
122		# remove groups without any elements in the original refs
123		# note: it's OK if groups have 0 value
124	32x	keep_grp <- vapply(grade_groups, function(x, rf) {
125	64x	any(x %in% rf)
126	32x	}, rf = names(refs_orig), logical(1))
127
128	32x	keep_el <- names(result) %in% names(refs_orig) \| names(result) %in% names(keep_grp)[keep_grp]
129	32x	result <- result[keep_el]
130
131	32x	result
132		}
133
134		#' @describeIn count_occurrences_by_grade Statistics function which counts the
135		#' number of patients by highest grade.
136		#'
137		#' @return
138		#' * `s_count_occurrences_by_grade()` returns a list of counts and fractions with one element per grade level or
139		#' grade level grouping.
140		#'
141		#' @examples
142		#' s_count_occurrences_by_grade(
143		#' df,
144		#' .N_col = 10L,
145		#' .var = "AETOXGR",
146		#' id = "USUBJID",
147		#' grade_groups = list("ANY" = levels(df$AETOXGR))
148		#' )
149		#'
150		#' @export
151		s_count_occurrences_by_grade <- function(df,
152		.var,
153		.N_row, # nolint
154		.N_col, # nolint
155		id = "USUBJID",
156		grade_groups = list(),
157		remove_single = TRUE,
158		only_grade_groups = FALSE,
159		denom = c("N_col", "n", "N_row"),
160		labelstr = "") {
161	75x	assert_valid_factor(df[[.var]])
162	75x	assert_df_with_variables(df, list(grade = .var, id = id))
163
164	75x	denom <- match.arg(denom) %>%
165	75x	switch(
166	75x	n = nlevels(factor(df[[id]])),
167	75x	N_row = .N_row,
168	75x	N_col = .N_col
169		)
170
171	75x	if (nrow(df) < 1) {
172	5x	grade_levels <- levels(df[[.var]])
173	5x	l_count <- as.list(rep(0, length(grade_levels)))
174	5x	names(l_count) <- grade_levels
175		} else {
176	70x	if (isTRUE(is.factor(df[[id]]))) {
177	!	assert_valid_factor(df[[id]], any.missing = FALSE)
178		} else {
179	70x	checkmate::assert_character(df[[id]], min.chars = 1, any.missing = FALSE)
180		}
181	70x	checkmate::assert_count(.N_col)
182
183	70x	id <- df[[id]]
184	70x	grade <- df[[.var]]
185
186	70x	if (!is.ordered(grade)) {
187	70x	grade_lbl <- obj_label(grade)
188	70x	lvls <- levels(grade)
189	70x	if (sum(grepl("^\\d+$", lvls)) %in% c(0, length(lvls))) {
190	69x	lvl_ord <- lvls
191		} else {
192	1x	lvls[!grepl("^\\d+$", lvls)] <- min(as.numeric(lvls[grepl("^\\d+$", lvls)])) - 1
193	1x	lvl_ord <- levels(grade)[order(as.numeric(lvls))]
194		}
195	70x	grade <- formatters::with_label(factor(grade, levels = lvl_ord, ordered = TRUE), grade_lbl)
196		}
197
198	70x	missing_lvl <- grepl("missing", tolower(levels(grade)))
199	70x	if (any(missing_lvl)) {
200	1x	grade <- factor(
201	1x	grade,
202	1x	levels = c(levels(grade)[!missing_lvl], levels(grade)[missing_lvl]),
203	1x	ordered = is.ordered(grade)
204		)
205		}
206	70x	df_max <- stats::aggregate(grade ~ id, FUN = max, drop = FALSE)
207	70x	l_count <- as.list(table(df_max$grade))
208		}
209
210	75x	if (length(grade_groups) > 0) {
211	30x	l_count <- h_append_grade_groups(grade_groups, l_count, remove_single, only_grade_groups)
212		}
213
214	75x	l_count_fraction <- lapply(
215	75x	l_count,
216	75x	function(i, denom) {
217	299x	if (i == 0 && denom == 0) {
218	9x	c(0, 0)
219		} else {
220	290x	c(i, i / denom)
221		}
222		},
223	75x	denom = denom
224		)
225
226	75x	list(
227	75x	count_fraction = l_count_fraction,
228	75x	count_fraction_fixed_dp = l_count_fraction
229		)
230		}
231
232		#' @describeIn count_occurrences_by_grade Formatted analysis function which is used as `afun`
233		#' in `count_occurrences_by_grade()`.
234		#'
235		#' @return
236		#' * `a_count_occurrences_by_grade()` returns the corresponding list with formatted [rtables::CellValue()].
237		#'
238		#' @examples
239		#' a_count_occurrences_by_grade(
240		#' df,
241		#' .N_col = 10L,
242		#' .N_row = 10L,
243		#' .var = "AETOXGR",
244		#' id = "USUBJID",
245		#' grade_groups = list("ANY" = levels(df$AETOXGR))
246		#' )
247		#'
248		#' @export
249		a_count_occurrences_by_grade <- function(df,
250		labelstr = "",
251		id = "USUBJID",
252		grade_groups = list(),
253		remove_single = TRUE,
254		only_grade_groups = FALSE,
255		denom = c("N_col", "n", "N_row"),
256		.N_col, # nolint
257		.N_row, # nolint
258		.df_row,
259		.var = NULL,
260		.stats = NULL,
261		.formats = NULL,
262		.labels = NULL,
263		.indent_mods = NULL,
264		na_str = default_na_str()) {
265	56x	x_stats <- s_count_occurrences_by_grade(
266	56x	df = df, .var = .var, .N_row = .N_row, .N_col = .N_col, id = id,
267	56x	grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups,
268	56x	denom = denom, labelstr = labelstr
269		)
270
271	56x	if (is.null(unlist(x_stats))) {
272	!	return(NULL)
273		}
274
275		# Fill in with formatting defaults if needed
276	56x	.stats <- get_stats("count_occurrences_by_grade", stats_in = .stats)
277	56x	x_stats <- x_stats[.stats]
278	56x	levels_per_stats <- lapply(x_stats, names)
279	56x	.formats <- get_formats_from_stats(.stats, .formats, levels_per_stats)
280	56x	.labels <- get_labels_from_stats(.stats, .labels, levels_per_stats)
281	56x	.indent_mods <- get_indents_from_stats(.stats, .indent_mods, levels_per_stats)
282
283		# Unlist stats
284	56x	x_stats <- x_stats %>% .unlist_keep_nulls()
285
286		# Auto format handling
287	56x	.formats <- apply_auto_formatting(.formats, x_stats, .df_row, .var)
288
289	56x	in_rows(
290	56x	.list = x_stats,
291	56x	.formats = .formats,
292	56x	.names = .labels %>% .unlist_keep_nulls(),
293	56x	.labels = .labels %>% .unlist_keep_nulls(),
294	56x	.indent_mods = .indent_mods %>% .unlist_keep_nulls(),
295	56x	.format_na_strs = na_str
296		)
297		}
298
299		#' @describeIn count_occurrences_by_grade Layout-creating function which can take statistics function
300		#' arguments and additional format arguments. This function is a wrapper for [rtables::analyze()].
301		#'
302		#' @return
303		#' * `count_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,
304		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted rows containing
305		#' the statistics from `s_count_occurrences_by_grade()` to the table layout.
306		#'
307		#' @examples
308		#' library(dplyr)
309		#'
310		#' df <- data.frame(
311		#' USUBJID = as.character(c(1:6, 1)),
312		#' ARM = factor(c("A", "A", "A", "B", "B", "B", "A"), levels = c("A", "B")),
313		#' AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),
314		#' AESEV = factor(
315		#' x = c("MILD", "MODERATE", "SEVERE", "MILD", "MILD", "MODERATE", "SEVERE"),
316		#' levels = c("MILD", "MODERATE", "SEVERE")
317		#' ),
318		#' stringsAsFactors = FALSE
319		#' )
320		#'
321		#' df_adsl <- df %>%
322		#' select(USUBJID, ARM) %>%
323		#' unique()
324		#'
325		#' # Layout creating function with custom format.
326		#' basic_table() %>%
327		#' split_cols_by("ARM") %>%
328		#' add_colcounts() %>%
329		#' count_occurrences_by_grade(
330		#' var = "AESEV",
331		#' .formats = c("count_fraction" = "xx.xx (xx.xx%)")
332		#' ) %>%
333		#' build_table(df, alt_counts_df = df_adsl)
334		#'
335		#' # Define additional grade groupings.
336		#' grade_groups <- list(
337		#' "-Any-" = c("1", "2", "3", "4", "5"),
338		#' "Grade 1-2" = c("1", "2"),
339		#' "Grade 3-5" = c("3", "4", "5")
340		#' )
341		#'
342		#' basic_table() %>%
343		#' split_cols_by("ARM") %>%
344		#' add_colcounts() %>%
345		#' count_occurrences_by_grade(
346		#' var = "AETOXGR",
347		#' grade_groups = grade_groups,
348		#' only_grade_groups = TRUE
349		#' ) %>%
350		#' build_table(df, alt_counts_df = df_adsl)
351		#'
352		#' @export
353		#' @order 2
354		count_occurrences_by_grade <- function(lyt,
355		var,
356		id = "USUBJID",
357		grade_groups = list(),
358		remove_single = TRUE,
359		only_grade_groups = FALSE,
360		var_labels = var,
361		show_labels = "default",
362		riskdiff = FALSE,
363		na_str = default_na_str(),
364		nested = TRUE,
365		...,
366		table_names = var,
367		.stats = "count_fraction",
368		.formats = list(count_fraction = format_count_fraction_fixed_dp),
369		.indent_mods = NULL,
370		.labels = NULL) {
371	12x	checkmate::assert_flag(riskdiff)
372	12x	extra_args <- list(
373	12x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
374		)
375	12x	s_args <- list(
376	12x	id = id, grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups, ...
377		)
378
379	12x	if (isFALSE(riskdiff)) {
380	10x	extra_args <- c(extra_args, s_args)
381		} else {
382	2x	extra_args <- c(
383	2x	extra_args,
384	2x	list(
385	2x	afun = list("s_count_occurrences_by_grade" = a_count_occurrences_by_grade),
386	2x	s_args = s_args
387		)
388		)
389		}
390
391	12x	analyze(
392	12x	lyt = lyt,
393	12x	vars = var,
394	12x	afun = ifelse(isFALSE(riskdiff), a_count_occurrences_by_grade, afun_riskdiff),
395	12x	var_labels = var_labels,
396	12x	show_labels = show_labels,
397	12x	table_names = table_names,
398	12x	na_str = na_str,
399	12x	nested = nested,
400	12x	extra_args = extra_args
401		)
402		}
403
404		#' @describeIn count_occurrences_by_grade Layout-creating function which can take content function arguments
405		#' and additional format arguments. This function is a wrapper for [rtables::summarize_row_groups()].
406		#'
407		#' @return
408		#' * `summarize_occurrences_by_grade()` returns a layout object suitable for passing to further layouting functions,
409		#' or to [rtables::build_table()]. Adding this function to an `rtable` layout will add formatted content rows
410		#' containing the statistics from `s_count_occurrences_by_grade()` to the table layout.
411		#'
412		#' @examples
413		#' # Layout creating function with custom format.
414		#' basic_table() %>%
415		#' add_colcounts() %>%
416		#' split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
417		#' summarize_occurrences_by_grade(
418		#' var = "AESEV",
419		#' .formats = c("count_fraction" = "xx.xx (xx.xx%)")
420		#' ) %>%
421		#' build_table(df, alt_counts_df = df_adsl)
422		#'
423		#' basic_table() %>%
424		#' add_colcounts() %>%
425		#' split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
426		#' summarize_occurrences_by_grade(
427		#' var = "AETOXGR",
428		#' grade_groups = grade_groups
429		#' ) %>%
430		#' build_table(df, alt_counts_df = df_adsl)
431		#'
432		#' @export
433		#' @order 3
434		summarize_occurrences_by_grade <- function(lyt,
435		var,
436		id = "USUBJID",
437		grade_groups = list(),
438		remove_single = TRUE,
439		only_grade_groups = FALSE,
440		riskdiff = FALSE,
441		na_str = default_na_str(),
442		...,
443		.stats = "count_fraction",
444		.formats = list(count_fraction = format_count_fraction_fixed_dp),
445		.indent_mods = NULL,
446		.labels = NULL) {
447	6x	checkmate::assert_flag(riskdiff)
448	6x	extra_args <- list(
449	6x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
450		)
451	6x	s_args <- list(
452	6x	id = id, grade_groups = grade_groups, remove_single = remove_single, only_grade_groups = only_grade_groups, ...
453		)
454
455	6x	if (isFALSE(riskdiff)) {
456	4x	extra_args <- c(extra_args, s_args)
457		} else {
458	2x	extra_args <- c(
459	2x	extra_args,
460	2x	list(
461	2x	afun = list("s_count_occurrences_by_grade" = a_count_occurrences_by_grade),
462	2x	s_args = s_args
463		)
464		)
465		}
466
467	6x	summarize_row_groups(
468	6x	lyt = lyt,
469	6x	var = var,
470	6x	cfun = ifelse(isFALSE(riskdiff), a_count_occurrences_by_grade, afun_riskdiff),
471	6x	na_str = na_str,
472	6x	extra_args = extra_args
473		)
474		}

1		#' Encode categorical missing values in a data frame
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' This is a helper function to encode missing entries across groups of categorical
6		#' variables in a data frame.
7		#'
8		#' @details Missing entries are those with `NA` or empty strings and will
9		#' be replaced with a specified value. If factor variables include missing
10		#' values, the missing value will be inserted as the last level.
11		#' Similarly, in case character or logical variables should be converted to factors
12		#' with the `char_as_factor` or `logical_as_factor` options, the missing values will
13		#' be set as the last level.
14		#'
15		#' @param data (`data.frame`)\cr data set.
16		#' @param omit_columns (`character`)\cr names of variables from `data` that should
17		#' not be modified by this function.
18		#' @param char_as_factor (`flag`)\cr whether to convert character variables
19		#' in `data` to factors.
20		#' @param logical_as_factor (`flag`)\cr whether to convert logical variables
21		#' in `data` to factors.
22		#' @param na_level (`string`)\cr string used to replace all `NA` or empty
23		#' values inside non-`omit_columns` columns.
24		#'
25		#' @return A `data.frame` with the chosen modifications applied.
26		#'
27		#' @seealso [sas_na()] and [explicit_na()] for other missing data helper functions.
28		#'
29		#' @examples
30		#' my_data <- data.frame(
31		#' u = c(TRUE, FALSE, NA, TRUE),
32		#' v = factor(c("A", NA, NA, NA), levels = c("Z", "A")),
33		#' w = c("A", "B", NA, "C"),
34		#' x = c("D", "E", "F", NA),
35		#' y = c("G", "H", "I", ""),
36		#' z = c(1, 2, 3, 4),
37		#' stringsAsFactors = FALSE
38		#' )
39		#'
40		#' # Example 1
41		#' # Encode missing values in all character or factor columns.
42		#' df_explicit_na(my_data)
43		#' # Also convert logical columns to factor columns.
44		#' df_explicit_na(my_data, logical_as_factor = TRUE)
45		#' # Encode missing values in a subset of columns.
46		#' df_explicit_na(my_data, omit_columns = c("x", "y"))
47		#'
48		#' # Example 2
49		#' # Here we purposefully convert all `M` values to `NA` in the `SEX` variable.
50		#' # After running `df_explicit_na` the `NA` values are encoded as `<Missing>` but they are not
51		#' # included when generating `rtables`.
52		#' adsl <- tern_ex_adsl
53		#' adsl$SEX[adsl$SEX == "M"] <- NA
54		#' adsl <- df_explicit_na(adsl)
55		#'
56		#' # If you want the `Na` values to be displayed in the table use the `na_level` argument.
57		#' adsl <- tern_ex_adsl
58		#' adsl$SEX[adsl$SEX == "M"] <- NA
59		#' adsl <- df_explicit_na(adsl, na_level = "Missing Values")
60		#'
61		#' # Example 3
62		#' # Numeric variables that have missing values are not altered. This means that any `NA` value in
63		#' # a numeric variable will not be included in the summary statistics, nor will they be included
64		#' # in the denominator value for calculating the percent values.
65		#' adsl <- tern_ex_adsl
66		#' adsl$AGE[adsl$AGE < 30] <- NA
67		#' adsl <- df_explicit_na(adsl)
68		#'
69		#' @export
70		df_explicit_na <- function(data,
71		omit_columns = NULL,
72		char_as_factor = TRUE,
73		logical_as_factor = FALSE,
74		na_level = "<Missing>") {
75	24x	checkmate::assert_character(omit_columns, null.ok = TRUE, min.len = 1, any.missing = FALSE)
76	23x	checkmate::assert_data_frame(data)
77	22x	checkmate::assert_flag(char_as_factor)
78	21x	checkmate::assert_flag(logical_as_factor)
79	21x	checkmate::assert_string(na_level)
80
81	19x	target_vars <- if (is.null(omit_columns)) {
82	17x	names(data)
83		} else {
84	2x	setdiff(names(data), omit_columns) # May have duplicates.
85		}
86	19x	if (length(target_vars) == 0) {
87	1x	return(data)
88		}
89
90	18x	l_target_vars <- split(target_vars, target_vars)
91
92		# Makes sure target_vars exist in data and names are not duplicated.
93	18x	assert_df_with_variables(data, l_target_vars)
94
95	18x	for (x in target_vars) {
96	306x	xi <- data[[x]]
97	306x	xi_label <- obj_label(xi)
98
99		# Determine whether to convert character or logical input.
100	306x	do_char_conversion <- is.character(xi) && char_as_factor
101	306x	do_logical_conversion <- is.logical(xi) && logical_as_factor
102
103		# Pre-convert logical to character to deal correctly with replacing NA
104		# values below.
105	306x	if (do_logical_conversion) {
106	2x	xi <- as.character(xi)
107		}
108
109	306x	if (is.factor(xi) \|\| is.character(xi)) {
110		# Handle empty strings and NA values.
111	219x	xi <- explicit_na(sas_na(xi), label = na_level)
112
113		# Convert to factors if requested for the original type,
114		# set na_level as the last value.
115	219x	if (do_char_conversion \|\| do_logical_conversion) {
116	78x	levels_xi <- setdiff(sort(unique(xi)), na_level)
117	78x	if (na_level %in% unique(xi)) {
118	18x	levels_xi <- c(levels_xi, na_level)
119		}
120
121	78x	xi <- factor(xi, levels = levels_xi)
122		}
123
124	219x	data[, x] <- formatters::with_label(xi, label = xi_label)
125		}
126		}
127	18x	return(data)
128		}

1		#' Helper function for deriving analysis datasets for select laboratory tables
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper function that merges ADSL and ADLB datasets so that missing lab test records are inserted in the
6		#' output dataset. Remember that `na_level` must match the needed pre-processing
7		#' done with [df_explicit_na()] to have the desired output.
8		#'
9		#' @param adsl (`data.frame`)\cr ADSL data frame.
10		#' @param adlb (`data.frame`)\cr ADLB data frame.
11		#' @param worst_flag (named `character`)\cr worst post-baseline lab flag variable. See how this is implemented in the
12		#' following examples.
13		#' @param by_visit (`flag`)\cr defaults to `FALSE` to generate worst grade per patient.
14		#' If worst grade per patient per visit is specified for `worst_flag`, then
15		#' `by_visit` should be `TRUE` to generate worst grade patient per visit.
16		#' @param no_fillin_visits (named `character`)\cr visits that are not considered for post-baseline worst toxicity
17		#' grade. Defaults to `c("SCREENING", "BASELINE")`.
18		#'
19		#' @return `df` containing variables shared between `adlb` and `adsl` along with variables `PARAM`, `PARAMCD`,
20		#' `ATOXGR`, and `BTOXGR` relevant for analysis. Optionally, `AVISIT` are `AVISITN` are included when
21		#' `by_visit = TRUE` and `no_fillin_visits = c("SCREENING", "BASELINE")`.
22		#'
23		#' @details In the result data missing records will be created for the following situations:
24		#' * Patients who are present in `adsl` but have no lab data in `adlb` (both baseline and post-baseline).
25		#' * Patients who do not have any post-baseline lab values.
26		#' * Patients without any post-baseline values flagged as the worst.
27		#'
28		#' @examples
29		#' # `h_adsl_adlb_merge_using_worst_flag`
30		#' adlb_out <- h_adsl_adlb_merge_using_worst_flag(
31		#' tern_ex_adsl,
32		#' tern_ex_adlb,
33		#' worst_flag = c("WGRHIFL" = "Y")
34		#' )
35		#'
36		#' # `h_adsl_adlb_merge_using_worst_flag` by visit example
37		#' adlb_out_by_visit <- h_adsl_adlb_merge_using_worst_flag(
38		#' tern_ex_adsl,
39		#' tern_ex_adlb,
40		#' worst_flag = c("WGRLOVFL" = "Y"),
41		#' by_visit = TRUE
42		#' )
43		#'
44		#' @export
45		h_adsl_adlb_merge_using_worst_flag <- function(adsl, # nolint
46		adlb,
47		worst_flag = c("WGRHIFL" = "Y"),
48		by_visit = FALSE,
49		no_fillin_visits = c("SCREENING", "BASELINE")) {
50	5x	col_names <- names(worst_flag)
51	5x	filter_values <- worst_flag
52
53	5x	temp <- Map(
54	5x	function(x, y) which(adlb[[x]] == y),
55	5x	col_names,
56	5x	filter_values
57		)
58
59	5x	position_satisfy_filters <- Reduce(intersect, temp)
60
61	5x	adsl_adlb_common_columns <- intersect(colnames(adsl), colnames(adlb))
62	5x	columns_from_adlb <- c("USUBJID", "PARAM", "PARAMCD", "AVISIT", "AVISITN", "ATOXGR", "BTOXGR")
63
64	5x	adlb_f <- adlb[position_satisfy_filters, ] %>%
65	5x	dplyr::filter(!.data[["AVISIT"]] %in% no_fillin_visits)
66	5x	adlb_f <- adlb_f[, columns_from_adlb]
67
68	5x	avisits_grid <- adlb %>%
69	5x	dplyr::filter(!.data[["AVISIT"]] %in% no_fillin_visits) %>%
70	5x	dplyr::pull(.data[["AVISIT"]]) %>%
71	5x	unique()
72
73	5x	if (by_visit) {
74	1x	adsl_lb <- expand.grid(
75	1x	USUBJID = unique(adsl$USUBJID),
76	1x	AVISIT = avisits_grid,
77	1x	PARAMCD = unique(adlb$PARAMCD)
78		)
79
80	1x	adsl_lb <- adsl_lb %>%
81	1x	dplyr::left_join(unique(adlb[c("AVISIT", "AVISITN")]), by = "AVISIT") %>%
82	1x	dplyr::left_join(unique(adlb[c("PARAM", "PARAMCD")]), by = "PARAMCD")
83
84	1x	adsl1 <- adsl[, adsl_adlb_common_columns]
85	1x	adsl_lb <- adsl1 %>% merge(adsl_lb, by = "USUBJID")
86
87	1x	by_variables_from_adlb <- c("USUBJID", "AVISIT", "AVISITN", "PARAMCD", "PARAM")
88
89	1x	adlb_btoxgr <- adlb %>%
90	1x	dplyr::select(c("USUBJID", "PARAMCD", "BTOXGR")) %>%
91	1x	unique() %>%
92	1x	dplyr::rename("BTOXGR_MAP" = "BTOXGR")
93
94	1x	adlb_out <- merge(
95	1x	adlb_f,
96	1x	adsl_lb,
97	1x	by = by_variables_from_adlb,
98	1x	all = TRUE,
99	1x	sort = FALSE
100		)
101	1x	adlb_out <- adlb_out %>%
102	1x	dplyr::left_join(adlb_btoxgr, by = c("USUBJID", "PARAMCD")) %>%
103	1x	dplyr::mutate(BTOXGR = .data$BTOXGR_MAP) %>%
104	1x	dplyr::select(-"BTOXGR_MAP")
105
106	1x	adlb_var_labels <- c(
107	1x	formatters::var_labels(adlb[by_variables_from_adlb]),
108	1x	formatters::var_labels(adlb[columns_from_adlb[!columns_from_adlb %in% by_variables_from_adlb]]),
109	1x	formatters::var_labels(adsl[adsl_adlb_common_columns[adsl_adlb_common_columns != "USUBJID"]])
110		)
111		} else {
112	4x	adsl_lb <- expand.grid(
113	4x	USUBJID = unique(adsl$USUBJID),
114	4x	PARAMCD = unique(adlb$PARAMCD)
115		)
116
117	4x	adsl_lb <- adsl_lb %>% dplyr::left_join(unique(adlb[c("PARAM", "PARAMCD")]), by = "PARAMCD")
118
119	4x	adsl1 <- adsl[, adsl_adlb_common_columns]
120	4x	adsl_lb <- adsl1 %>% merge(adsl_lb, by = "USUBJID")
121
122	4x	by_variables_from_adlb <- c("USUBJID", "PARAMCD", "PARAM")
123
124	4x	adlb_out <- merge(
125	4x	adlb_f,
126	4x	adsl_lb,
127	4x	by = by_variables_from_adlb,
128	4x	all = TRUE,
129	4x	sort = FALSE
130		)
131
132	4x	adlb_var_labels <- c(
133	4x	formatters::var_labels(adlb[by_variables_from_adlb]),
134	4x	formatters::var_labels(adlb[columns_from_adlb[!columns_from_adlb %in% by_variables_from_adlb]]),
135	4x	formatters::var_labels(adsl[adsl_adlb_common_columns[adsl_adlb_common_columns != "USUBJID"]])
136		)
137		}
138
139	5x	adlb_out$ATOXGR <- as.factor(adlb_out$ATOXGR)
140	5x	adlb_out$BTOXGR <- as.factor(adlb_out$BTOXGR)
141
142	5x	formatters::var_labels(adlb_out) <- adlb_var_labels
143
144	5x	adlb_out
145		}

1		#' Tabulate survival duration by subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The [tabulate_survival_subgroups()] function creates a layout element to tabulate survival duration by subgroup,
6		#' returning statistics including median survival time and hazard ratio for each population subgroup. The table is
7		#' created from `df`, a list of data frames returned by [extract_survival_subgroups()], with the statistics to include
8		#' specified via the `vars` parameter.
9		#'
10		#' A forest plot can be created from the resulting table using the [g_forest()] function.
11		#'
12		#' @inheritParams argument_convention
13		#' @inheritParams survival_coxph_pairwise
14		#' @param df (`list`)\cr list of data frames containing all analysis variables. List should be
15		#' created using [extract_survival_subgroups()].
16		#' @param vars (`character`)\cr the names of statistics to be reported among:
17		#' * `n_tot_events`: Total number of events per group.
18		#' * `n_events`: Number of events per group.
19		#' * `n_tot`: Total number of observations per group.
20		#' * `n`: Number of observations per group.
21		#' * `median`: Median survival time.
22		#' * `hr`: Hazard ratio.
23		#' * `ci`: Confidence interval of hazard ratio.
24		#' * `pval`: p-value of the effect.
25		#' Note, one of the statistics `n_tot` and `n_tot_events`, as well as both `hr` and `ci`
26		#' are required.
27		#' @param time_unit (`string`)\cr label with unit of median survival time. Default `NULL` skips displaying unit.
28		#'
29		#' @details These functions create a layout starting from a data frame which contains
30		#' the required statistics. Tables typically used as part of forest plot.
31		#'
32		#' @seealso [extract_survival_subgroups()]
33		#'
34		#' @examples
35		#' library(dplyr)
36		#'
37		#' adtte <- tern_ex_adtte
38		#'
39		#' # Save variable labels before data processing steps.
40		#' adtte_labels <- formatters::var_labels(adtte)
41		#'
42		#' adtte_f <- adtte %>%
43		#' filter(
44		#' PARAMCD == "OS",
45		#' ARM %in% c("B: Placebo", "A: Drug X"),
46		#' SEX %in% c("M", "F")
47		#' ) %>%
48		#' mutate(
49		#' # Reorder levels of ARM to display reference arm before treatment arm.
50		#' ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
51		#' SEX = droplevels(SEX),
52		#' AVALU = as.character(AVALU),
53		#' is_event = CNSR == 0
54		#' )
55		#' labels <- c(
56		#' "ARM" = adtte_labels[["ARM"]],
57		#' "SEX" = adtte_labels[["SEX"]],
58		#' "AVALU" = adtte_labels[["AVALU"]],
59		#' "is_event" = "Event Flag"
60		#' )
61		#' formatters::var_labels(adtte_f)[names(labels)] <- labels
62		#'
63		#' df <- extract_survival_subgroups(
64		#' variables = list(
65		#' tte = "AVAL",
66		#' is_event = "is_event",
67		#' arm = "ARM", subgroups = c("SEX", "BMRKR2")
68		#' ),
69		#' label_all = "Total Patients",
70		#' data = adtte_f
71		#' )
72		#' df
73		#'
74		#' df_grouped <- extract_survival_subgroups(
75		#' variables = list(
76		#' tte = "AVAL",
77		#' is_event = "is_event",
78		#' arm = "ARM", subgroups = c("SEX", "BMRKR2")
79		#' ),
80		#' data = adtte_f,
81		#' groups_lists = list(
82		#' BMRKR2 = list(
83		#' "low" = "LOW",
84		#' "low/medium" = c("LOW", "MEDIUM"),
85		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
86		#' )
87		#' )
88		#' )
89		#' df_grouped
90		#'
91		#' @name survival_duration_subgroups
92		#' @order 1
93		NULL
94
95		#' Prepare survival data for population subgroups in data frames
96		#'
97		#' @description `r lifecycle::badge("stable")`
98		#'
99		#' Prepares estimates of median survival times and treatment hazard ratios for population subgroups in
100		#' data frames. Simple wrapper for [h_survtime_subgroups_df()] and [h_coxph_subgroups_df()]. Result is a `list`
101		#' of two `data.frame`s: `survtime` and `hr`. `variables` corresponds to the names of variables found in `data`,
102		#' passed as a named `list` and requires elements `tte`, `is_event`, `arm` and optionally `subgroups` and `strata`.
103		#' `groups_lists` optionally specifies groupings for `subgroups` variables.
104		#'
105		#' @inheritParams argument_convention
106		#' @inheritParams survival_duration_subgroups
107		#' @inheritParams survival_coxph_pairwise
108		#'
109		#' @return A named `list` of two elements:
110		#' * `survtime`: A `data.frame` containing columns `arm`, `n`, `n_events`, `median`, `subgroup`, `var`,
111		#' `var_label`, and `row_type`.
112		#' * `hr`: A `data.frame` containing columns `arm`, `n_tot`, `n_tot_events`, `hr`, `lcl`, `ucl`, `conf_level`,
113		#' `pval`, `pval_label`, `subgroup`, `var`, `var_label`, and `row_type`.
114		#'
115		#' @seealso [survival_duration_subgroups]
116		#'
117		#' @export
118		extract_survival_subgroups <- function(variables,
119		data,
120		groups_lists = list(),
121		control = control_coxph(),
122		label_all = "All Patients") {
123	12x	if ("strat" %in% names(variables)) {
124	!	warning(
125	!	"Warning: the `strat` element name of the `variables` list argument to `extract_survival_subgroups() ",
126	!	"was deprecated in tern 0.9.4.\n ",
127	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
128		)
129	!	variables[["strata"]] <- variables[["strat"]]
130		}
131
132	12x	df_survtime <- h_survtime_subgroups_df(
133	12x	variables,
134	12x	data,
135	12x	groups_lists = groups_lists,
136	12x	label_all = label_all
137		)
138	12x	df_hr <- h_coxph_subgroups_df(
139	12x	variables,
140	12x	data,
141	12x	groups_lists = groups_lists,
142	12x	control = control,
143	12x	label_all = label_all
144		)
145
146	12x	list(survtime = df_survtime, hr = df_hr)
147		}
148
149		#' @describeIn survival_duration_subgroups Formatted analysis function which is used as
150		#' `afun` in `tabulate_survival_subgroups()`.
151		#'
152		#' @return
153		#' * `a_survival_subgroups()` returns the corresponding list with formatted [rtables::CellValue()].
154		#'
155		#' @keywords internal
156		a_survival_subgroups <- function(.formats = list( # nolint start
157		n = "xx",
158		n_events = "xx",
159		n_tot_events = "xx",
160		median = "xx.x",
161		n_tot = "xx",
162		hr = list(format_extreme_values(2L)),
163		ci = list(format_extreme_values_ci(2L)),
164		pval = "x.xxxx \| (<0.0001)"
165		),
166		na_str = default_na_str()) { # nolint end
167	21x	checkmate::assert_list(.formats)
168	21x	checkmate::assert_subset(
169	21x	names(.formats),
170	21x	c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval", "riskdiff")
171		)
172
173	21x	afun_lst <- Map(
174	21x	function(stat, fmt, na_str) {
175	160x	function(df, labelstr = "", ...) {
176	312x	in_rows(
177	312x	.list = as.list(df[[stat]]),
178	312x	.labels = as.character(df$subgroup),
179	312x	.formats = fmt,
180	312x	.format_na_strs = na_str
181		)
182		}
183		},
184	21x	stat = names(.formats),
185	21x	fmt = .formats,
186	21x	na_str = na_str
187		)
188
189	21x	afun_lst
190		}
191
192		#' @describeIn survival_duration_subgroups Table-creating function which creates a table
193		#' summarizing survival by subgroup. This function is a wrapper for [rtables::analyze_colvars()]
194		#' and [rtables::summarize_row_groups()].
195		#'
196		#' @param label_all `r lifecycle::badge("deprecated")`\cr please assign the `label_all` parameter within the
197		#' [extract_survival_subgroups()] function when creating `df`.
198		#' @param riskdiff (`list`)\cr if a risk (proportion) difference column should be added, a list of settings to apply
199		#' within the column. See [control_riskdiff()] for details. If `NULL`, no risk difference column will be added. If
200		#' `riskdiff$arm_x` and `riskdiff$arm_y` are `NULL`, the first level of `df$survtime$arm` will be used as `arm_x`
201		#' and the second level as `arm_y`.
202		#'
203		#' @return An `rtables` table summarizing survival by subgroup.
204		#'
205		#' @examples
206		#' ## Table with default columns.
207		#' basic_table() %>%
208		#' tabulate_survival_subgroups(df, time_unit = adtte_f$AVALU[1])
209		#'
210		#' ## Table with a manually chosen set of columns: adding "pval".
211		#' basic_table() %>%
212		#' tabulate_survival_subgroups(
213		#' df = df,
214		#' vars = c("n_tot_events", "n_events", "median", "hr", "ci", "pval"),
215		#' time_unit = adtte_f$AVALU[1]
216		#' )
217		#'
218		#' @export
219		#' @order 2
220		tabulate_survival_subgroups <- function(lyt,
221		df,
222		vars = c("n_tot_events", "n_events", "median", "hr", "ci"),
223		groups_lists = list(),
224		label_all = lifecycle::deprecated(),
225		time_unit = NULL,
226		riskdiff = NULL,
227		na_str = default_na_str(),
228		.formats = c(
229		n = "xx", n_events = "xx", n_tot_events = "xx", median = "xx.x", n_tot = "xx",
230		hr = list(format_extreme_values(2L)), ci = list(format_extreme_values_ci(2L)),
231		pval = "x.xxxx \| (<0.0001)"
232		)) {
233	10x	checkmate::assert_list(riskdiff, null.ok = TRUE)
234	10x	checkmate::assert_true(any(c("n_tot", "n_tot_events") %in% vars))
235	10x	checkmate::assert_true(all(c("hr", "ci") %in% vars))
236
237	10x	if (lifecycle::is_present(label_all)) {
238	1x	lifecycle::deprecate_warn(
239	1x	"0.9.5", "tabulate_survival_subgroups(label_all)",
240	1x	details =
241	1x	"Please assign the `label_all` parameter within the `extract_survival_subgroups()` function when creating `df`."
242		)
243		}
244
245		# Create "ci" column from "lcl" and "ucl"
246	10x	df$hr$ci <- combine_vectors(df$hr$lcl, df$hr$ucl)
247
248		# Fill in missing formats with defaults
249	10x	default_fmts <- eval(formals(tabulate_survival_subgroups)$.formats)
250	10x	.formats <- c(.formats, default_fmts[vars[!vars %in% names(.formats)]])
251
252		# Extract additional parameters from df
253	10x	conf_level <- df$hr$conf_level[1]
254	10x	method <- df$hr$pval_label[1]
255	10x	colvars <- d_survival_subgroups_colvars(vars, conf_level = conf_level, method = method, time_unit = time_unit)
256	10x	survtime_vars <- intersect(colvars$vars, c("n", "n_events", "median"))
257	10x	hr_vars <- intersect(names(colvars$labels), c("n_tot", "n_tot_events", "hr", "ci", "pval"))
258	10x	colvars_survtime <- list(vars = survtime_vars, labels = colvars$labels[survtime_vars])
259	10x	colvars_hr <- list(vars = hr_vars, labels = colvars$labels[hr_vars])
260
261	10x	extra_args <- list(groups_lists = groups_lists, conf_level = conf_level, method = method)
262
263		# Get analysis function for each statistic
264	10x	afun_lst <- a_survival_subgroups(.formats = c(.formats, riskdiff = riskdiff$format), na_str = na_str)
265
266		# Add risk difference column
267	10x	if (!is.null(riskdiff)) {
268	1x	if (is.null(riskdiff$arm_x)) riskdiff$arm_x <- levels(df$survtime$arm)[1]
269	1x	if (is.null(riskdiff$arm_y)) riskdiff$arm_y <- levels(df$survtime$arm)[2]
270	1x	colvars_hr$vars <- c(colvars_hr$vars, "riskdiff")
271	1x	colvars_hr$labels <- c(colvars_hr$labels, riskdiff = riskdiff$col_label)
272	1x	arm_cols <- paste(rep(c("n_events", "n_events", "n", "n")), c(riskdiff$arm_x, riskdiff$arm_y), sep = "_")
273
274	1x	df_prop_diff <- df$survtime %>%
275	1x	dplyr::select(-"median") %>%
276	1x	tidyr::pivot_wider(
277	1x	id_cols = c("subgroup", "var", "var_label", "row_type"),
278	1x	names_from = "arm",
279	1x	values_from = c("n", "n_events")
280		) %>%
281	1x	dplyr::rowwise() %>%
282	1x	dplyr::mutate(
283	1x	riskdiff = stat_propdiff_ci(
284	1x	x = as.list(.data[[arm_cols[1]]]),
285	1x	y = as.list(.data[[arm_cols[2]]]),
286	1x	N_x = .data[[arm_cols[3]]],
287	1x	N_y = .data[[arm_cols[4]]]
288		)
289		) %>%
290	1x	dplyr::select(-dplyr::all_of(arm_cols))
291
292	1x	df$hr <- df$hr %>%
293	1x	dplyr::left_join(
294	1x	df_prop_diff,
295	1x	by = c("subgroup", "var", "var_label", "row_type")
296		)
297		}
298
299		# Add columns from table_survtime (optional)
300	10x	if (length(colvars_survtime$vars) > 0) {
301	9x	lyt_survtime <- split_cols_by(lyt = lyt, var = "arm")
302	9x	lyt_survtime <- split_rows_by(
303	9x	lyt = lyt_survtime,
304	9x	var = "row_type",
305	9x	split_fun = keep_split_levels("content"),
306	9x	nested = FALSE
307		)
308
309		# Add "All Patients" row
310	9x	lyt_survtime <- summarize_row_groups(
311	9x	lyt = lyt_survtime,
312	9x	var = "var_label",
313	9x	cfun = afun_lst[names(colvars_survtime$labels)],
314	9x	na_str = na_str,
315	9x	extra_args = extra_args
316		)
317	9x	lyt_survtime <- split_cols_by_multivar(
318	9x	lyt = lyt_survtime,
319	9x	vars = colvars_survtime$vars,
320	9x	varlabels = colvars_survtime$labels
321		)
322
323		# Add analysis rows
324	9x	if ("analysis" %in% df$survtime$row_type) {
325	8x	lyt_survtime <- split_rows_by(
326	8x	lyt = lyt_survtime,
327	8x	var = "row_type",
328	8x	split_fun = keep_split_levels("analysis"),
329	8x	nested = FALSE,
330	8x	child_labels = "hidden"
331		)
332	8x	lyt_survtime <- split_rows_by(lyt = lyt_survtime, var = "var_label", nested = TRUE)
333	8x	lyt_survtime <- analyze_colvars(
334	8x	lyt = lyt_survtime,
335	8x	afun = afun_lst[names(colvars_survtime$labels)],
336	8x	na_str = na_str,
337	8x	inclNAs = TRUE,
338	8x	extra_args = extra_args
339		)
340		}
341
342	9x	table_survtime <- build_table(lyt_survtime, df = df$survtime)
343		} else {
344	1x	table_survtime <- NULL
345		}
346
347		# Add columns from table_hr ("n_tot_events" or "n_tot", "or" and "ci" required)
348	10x	lyt_hr <- split_cols_by(lyt = lyt, var = "arm")
349	10x	lyt_hr <- split_rows_by(
350	10x	lyt = lyt_hr,
351	10x	var = "row_type",
352	10x	split_fun = keep_split_levels("content"),
353	10x	nested = FALSE
354		)
355	10x	lyt_hr <- summarize_row_groups(
356	10x	lyt = lyt_hr,
357	10x	var = "var_label",
358	10x	cfun = afun_lst[names(colvars_hr$labels)],
359	10x	na_str = na_str,
360	10x	extra_args = extra_args
361		)
362	10x	lyt_hr <- split_cols_by_multivar(
363	10x	lyt = lyt_hr,
364	10x	vars = colvars_hr$vars,
365	10x	varlabels = colvars_hr$labels
366		) %>%
367	10x	append_topleft("Baseline Risk Factors")
368
369		# Add analysis rows
370	10x	if ("analysis" %in% df$survtime$row_type) {
371	9x	lyt_hr <- split_rows_by(
372	9x	lyt = lyt_hr,
373	9x	var = "row_type",
374	9x	split_fun = keep_split_levels("analysis"),
375	9x	nested = FALSE,
376	9x	child_labels = "hidden"
377		)
378	9x	lyt_hr <- split_rows_by(lyt = lyt_hr, var = "var_label", nested = TRUE)
379	9x	lyt_hr <- analyze_colvars(
380	9x	lyt = lyt_hr,
381	9x	afun = afun_lst[names(colvars_hr$labels)],
382	9x	na_str = na_str,
383	9x	inclNAs = TRUE,
384	9x	extra_args = extra_args
385		)
386		}
387
388	10x	table_hr <- build_table(lyt_hr, df = df$hr)
389
390		# Join tables, add forest plot attributes
391	10x	n_tot_ids <- grep("^n_tot", colvars_hr$vars)
392	10x	if (is.null(table_survtime)) {
393	1x	result <- table_hr
394	1x	hr_id <- match("hr", colvars_hr$vars)
395	1x	ci_id <- match("ci", colvars_hr$vars)
396		} else {
397	9x	result <- cbind_rtables(table_hr[, n_tot_ids], table_survtime, table_hr[, -n_tot_ids])
398	9x	hr_id <- length(n_tot_ids) + ncol(table_survtime) + match("hr", colvars_hr$vars[-n_tot_ids])
399	9x	ci_id <- length(n_tot_ids) + ncol(table_survtime) + match("ci", colvars_hr$vars[-n_tot_ids])
400	9x	n_tot_ids <- seq_along(n_tot_ids)
401		}
402	10x	structure(
403	10x	result,
404	10x	forest_header = paste0(rev(levels(df$survtime$arm)), "\nBetter"),
405	10x	col_x = hr_id,
406	10x	col_ci = ci_id,
407	10x	col_symbol_size = n_tot_ids[1] # for scaling the symbol sizes in forest plots
408		)
409		}
410
411		#' Labels for column variables in survival duration by subgroup table
412		#'
413		#' @description `r lifecycle::badge("stable")`
414		#'
415		#' Internal function to check variables included in [tabulate_survival_subgroups()] and create column labels.
416		#'
417		#' @inheritParams tabulate_survival_subgroups
418		#' @inheritParams argument_convention
419		#' @param method (`string`)\cr p-value method for testing hazard ratio = 1.
420		#'
421		#' @return A `list` of variables and their labels to tabulate.
422		#'
423		#' @note At least one of `n_tot` and `n_tot_events` must be provided in `vars`.
424		#'
425		#' @export
426		d_survival_subgroups_colvars <- function(vars,
427		conf_level,
428		method,
429		time_unit = NULL) {
430	21x	checkmate::assert_character(vars)
431	21x	checkmate::assert_string(time_unit, null.ok = TRUE)
432	21x	checkmate::assert_subset(c("hr", "ci"), vars)
433	21x	checkmate::assert_true(any(c("n_tot", "n_tot_events") %in% vars))
434	21x	checkmate::assert_subset(
435	21x	vars,
436	21x	c("n", "n_events", "median", "n_tot", "n_tot_events", "hr", "ci", "pval")
437		)
438
439	21x	propcase_time_label <- if (!is.null(time_unit)) {
440	20x	paste0("Median (", time_unit, ")")
441		} else {
442	1x	"Median"
443		}
444
445	21x	varlabels <- c(
446	21x	n = "n",
447	21x	n_events = "Events",
448	21x	median = propcase_time_label,
449	21x	n_tot = "Total n",
450	21x	n_tot_events = "Total Events",
451	21x	hr = "Hazard Ratio",
452	21x	ci = paste0(100 * conf_level, "% Wald CI"),
453	21x	pval = method
454		)
455
456	21x	colvars <- vars
457
458		# The `lcl` variable is just a placeholder available in the analysis data,
459		# it is not acutally used in the tabulation.
460		# Variables used in the tabulation are lcl and ucl, see `a_survival_subgroups` for details.
461	21x	colvars[colvars == "ci"] <- "lcl"
462
463	21x	list(
464	21x	vars = colvars,
465	21x	labels = varlabels[vars]
466		)
467		}

1		#' Split function to configure risk difference column
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Wrapper function for [rtables::add_combo_levels()] which configures settings for the risk difference
6		#' column to be added to an `rtables` object. To add a risk difference column to a table, this function
7		#' should be used as `split_fun` in calls to [rtables::split_cols_by()], followed by setting argument
8		#' `riskdiff` to `TRUE` in all following analyze function calls.
9		#'
10		#' @param arm_x (`string`)\cr name of reference arm to use in risk difference calculations.
11		#' @param arm_y (`character`)\cr names of one or more arms to compare to reference arm in risk difference
12		#' calculations. A new column will be added for each value of `arm_y`.
13		#' @param col_label (`character`)\cr labels to use when rendering the risk difference column within the table.
14		#' If more than one comparison arm is specified in `arm_y`, default labels will specify which two arms are
15		#' being compared (reference arm vs. comparison arm).
16		#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
17		#'
18		#' @return A closure suitable for use as a split function (`split_fun`) within [rtables::split_cols_by()]
19		#' when creating a table layout.
20		#'
21		#' @seealso [stat_propdiff_ci()] for details on risk difference calculation.
22		#'
23		#' @examples
24		#' adae <- tern_ex_adae
25		#' adae$AESEV <- factor(adae$AESEV)
26		#'
27		#' lyt <- basic_table() %>%
28		#' split_cols_by("ARMCD", split_fun = add_riskdiff(arm_x = "ARM A", arm_y = c("ARM B", "ARM C"))) %>%
29		#' count_occurrences_by_grade(
30		#' var = "AESEV",
31		#' riskdiff = TRUE
32		#' )
33		#'
34		#' tbl <- build_table(lyt, df = adae)
35		#' tbl
36		#'
37		#' @export
38		add_riskdiff <- function(arm_x,
39		arm_y,
40		col_label = paste0(
41		"Risk Difference (%) (95% CI)", if (length(arm_y) > 1) paste0("\n", arm_x, " vs. ", arm_y)
42		),
43		pct = TRUE) {
44	19x	checkmate::assert_character(arm_x, len = 1)
45	19x	checkmate::assert_character(arm_y, min.len = 1)
46	19x	checkmate::assert_character(col_label, len = length(arm_y))
47
48	19x	combodf <- tibble::tribble(~valname, ~label, ~levelcombo, ~exargs)
49	19x	for (i in seq_len(length(arm_y))) {
50	20x	combodf <- rbind(
51	20x	combodf,
52	20x	tibble::tribble(
53	20x	~valname, ~label, ~levelcombo, ~exargs,
54	20x	paste("riskdiff", arm_x, arm_y[i], sep = "_"), col_label[i], c(arm_x, arm_y[i]), list()
55		)
56		)
57		}
58	19x	if (pct) combodf$valname <- paste0(combodf$valname, "_pct")
59	19x	add_combo_levels(combodf)
60		}
61
62		#' Analysis function to calculate risk difference column values
63		#'
64		#' In the risk difference column, this function uses the statistics function associated with `afun` to
65		#' calculates risk difference values from arm X (reference group) and arm Y. These arms are specified
66		#' when configuring the risk difference column which is done using the [add_riskdiff()] split function in
67		#' the previous call to [rtables::split_cols_by()]. For all other columns, applies `afun` as usual. This
68		#' function utilizes the [stat_propdiff_ci()] function to perform risk difference calculations.
69		#'
70		#' @inheritParams argument_convention
71		#' @param afun (named `list`)\cr a named list containing one name-value pair where the name corresponds to
72		#' the name of the statistics function that should be used in calculations and the value is the corresponding
73		#' analysis function.
74		#' @param s_args (named `list`)\cr additional arguments to be passed to the statistics function and analysis
75		#' function supplied in `afun`.
76		#'
77		#' @return A list of formatted [rtables::CellValue()].
78		#'
79		#' @seealso
80		#' * [stat_propdiff_ci()] for details on risk difference calculation.
81		#' * Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()] with
82		#' `riskdiff` argument set to `TRUE` in subsequent analyze functions calls, adds a risk difference column
83		#' to a table layout.
84		#'
85		#' @keywords internal
86		afun_riskdiff <- function(df,
87		labelstr = "",
88		.var,
89		.N_col, # nolint
90		.N_row, # nolint
91		.df_row,
92		.spl_context,
93		.all_col_counts,
94		.stats,
95		.formats = NULL,
96		.labels = NULL,
97		.indent_mods = NULL,
98		na_str = default_na_str(),
99		afun,
100		s_args = list()) {
101	146x	if (!any(grepl("riskdiff", names(.spl_context)))) {
102	!	stop(
103	!	"Please set up levels to use in risk difference calculations using the `add_riskdiff` ",
104	!	"split function within `split_cols_by`. See ?add_riskdiff for details."
105		)
106		}
107	146x	checkmate::assert_list(afun, len = 1, types = "function")
108	146x	checkmate::assert_named(afun)
109	146x	afun_args <- list(
110	146x	.var = .var, .df_row = .df_row, .N_row = .N_row, denom = "N_col", labelstr = labelstr,
111	146x	.stats = .stats, .formats = .formats, .labels = .labels, .indent_mods = .indent_mods, na_str = na_str
112		)
113	146x	afun_args <- afun_args[intersect(names(afun_args), names(as.list(args(afun[[1]]))))]
114	!	if ("denom" %in% names(s_args)) afun_args[["denom"]] <- NULL
115
116	146x	cur_split <- tail(.spl_context$cur_col_split_val[[1]], 1)
117	146x	if (!grepl("^riskdiff", cur_split)) {
118		# Apply basic afun (no risk difference) in all other columns
119	108x	do.call(afun[[1]], args = c(list(df = df, .N_col = .N_col), afun_args, s_args))
120		} else {
121	38x	arm_x <- strsplit(cur_split, "_")[[1]][2]
122	38x	arm_y <- strsplit(cur_split, "_")[[1]][3]
123	38x	if (length(.spl_context$cur_col_split[[1]]) > 1) { # Different split name for nested column splits
124	8x	arm_spl_x <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 2)], collapse = ""))
125	8x	arm_spl_y <- gsub("riskdiff", "", paste0(strsplit(.spl_context$cur_col_id[1], "_")[[1]][c(1, 3)], collapse = ""))
126		} else {
127	30x	arm_spl_x <- arm_x
128	30x	arm_spl_y <- arm_y
129		}
130
131	38x	N_col_x <- .all_col_counts[[arm_spl_x]] # nolint
132	38x	N_col_y <- .all_col_counts[[arm_spl_y]] # nolint
133	38x	cur_var <- tail(.spl_context$cur_col_split[[1]], 1)
134
135		# Apply statistics function to arm X and arm Y data
136	38x	s_args <- c(s_args, afun_args[intersect(names(afun_args), names(as.list(args(names(afun)))))])
137	38x	s_x <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_x, ], .N_col = N_col_x), s_args))
138	38x	s_y <- do.call(names(afun), args = c(list(df = df[df[[cur_var]] == arm_y, ], .N_col = N_col_y), s_args))
139
140		# Get statistic name and row names
141	38x	stat <- ifelse("count_fraction" %in% names(s_x), "count_fraction", "unique")
142	38x	if ("flag_variables" %in% names(s_args)) {
143	2x	var_nms <- s_args$flag_variables
144	36x	} else if (is.list(s_x[[stat]]) && !is.null(names(s_x[[stat]]))) {
145	24x	var_nms <- names(s_x[[stat]])
146		} else {
147	12x	var_nms <- ""
148	12x	s_x[[stat]] <- list(s_x[[stat]])
149	12x	s_y[[stat]] <- list(s_y[[stat]])
150		}
151
152		# Calculate risk difference for each row, repeated if multiple statistics in table
153	38x	pct <- tail(strsplit(cur_split, "_")[[1]], 1) == "pct"
154	38x	rd_ci <- rep(stat_propdiff_ci(
155	38x	lapply(s_x[[stat]], `[`, 1), lapply(s_y[[stat]], `[`, 1),
156	38x	N_col_x, N_col_y,
157	38x	list_names = var_nms,
158	38x	pct = pct
159	38x	), max(1, length(.stats)))
160
161	38x	in_rows(.list = rd_ci, .formats = "xx.x (xx.x - xx.x)", .indent_mods = .indent_mods)
162		}
163		}
164
165		#' Control function for risk difference column
166		#'
167		#' @description `r lifecycle::badge("stable")`
168		#'
169		#' Sets a list of parameters to use when generating a risk (proportion) difference column. Used as input to the
170		#' `riskdiff` parameter of [tabulate_rsp_subgroups()] and [tabulate_survival_subgroups()].
171		#'
172		#' @inheritParams add_riskdiff
173		#' @param format (`string` or `function`)\cr the format label (string) or formatting function to apply to the risk
174		#' difference statistic. See the `3d` string options in [formatters::list_valid_format_labels()] for possible format
175		#' strings. Defaults to `"xx.x (xx.x - xx.x)"`.
176		#'
177		#' @return A `list` of items with names corresponding to the arguments.
178		#'
179		#' @seealso [add_riskdiff()], [tabulate_rsp_subgroups()], and [tabulate_survival_subgroups()].
180		#'
181		#' @examples
182		#' control_riskdiff()
183		#' control_riskdiff(arm_x = "ARM A", arm_y = "ARM B")
184		#'
185		#' @export
186		control_riskdiff <- function(arm_x = NULL,
187		arm_y = NULL,
188		format = "xx.x (xx.x - xx.x)",
189		col_label = "Risk Difference (%) (95% CI)",
190		pct = TRUE) {
191	2x	checkmate::assert_character(arm_x, len = 1, null.ok = TRUE)
192	2x	checkmate::assert_character(arm_y, min.len = 1, null.ok = TRUE)
193	2x	checkmate::assert_character(format, len = 1)
194	2x	checkmate::assert_character(col_label)
195	2x	checkmate::assert_flag(pct)
196
197	2x	list(arm_x = arm_x, arm_y = arm_y, format = format, col_label = col_label, pct = pct)
198		}

1		#' Tabulate biomarker effects on binary response by subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' The [tabulate_rsp_biomarkers()] function creates a layout element to tabulate the estimated biomarker effects on a
6		#' binary response endpoint across subgroups, returning statistics including response rate and odds ratio for each
7		#' population subgroup. The table is created from `df`, a list of data frames returned by [extract_rsp_biomarkers()],
8		#' with the statistics to include specified via the `vars` parameter.
9		#'
10		#' A forest plot can be created from the resulting table using the [g_forest()] function.
11		#'
12		#' @inheritParams argument_convention
13		#' @param df (`data.frame`)\cr containing all analysis variables, as returned by
14		#' [extract_rsp_biomarkers()].
15		#' @param vars (`character`)\cr the names of statistics to be reported among:
16		#' * `n_tot`: Total number of patients per group.
17		#' * `n_rsp`: Total number of responses per group.
18		#' * `prop`: Total response proportion per group.
19		#' * `or`: Odds ratio.
20		#' * `ci`: Confidence interval of odds ratio.
21		#' * `pval`: p-value of the effect.
22		#' Note, the statistics `n_tot`, `or` and `ci` are required.
23		#'
24		#' @return An `rtables` table summarizing biomarker effects on binary response by subgroup.
25		#'
26		#' @details These functions create a layout starting from a data frame which contains
27		#' the required statistics. The tables are then typically used as input for forest plots.
28		#'
29		#' @note In contrast to [tabulate_rsp_subgroups()] this tabulation function does
30		#' not start from an input layout `lyt`. This is because internally the table is
31		#' created by combining multiple subtables.
32		#'
33		#' @seealso [h_tab_rsp_one_biomarker()] which is used internally, [extract_rsp_biomarkers()].
34		#'
35		#' @examples
36		#' library(dplyr)
37		#' library(forcats)
38		#'
39		#' adrs <- tern_ex_adrs
40		#' adrs_labels <- formatters::var_labels(adrs)
41		#'
42		#' adrs_f <- adrs %>%
43		#' filter(PARAMCD == "BESRSPI") %>%
44		#' mutate(rsp = AVALC == "CR")
45		#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
46		#'
47		#' df <- extract_rsp_biomarkers(
48		#' variables = list(
49		#' rsp = "rsp",
50		#' biomarkers = c("BMRKR1", "AGE"),
51		#' covariates = "SEX",
52		#' subgroups = "BMRKR2"
53		#' ),
54		#' data = adrs_f
55		#' )
56		#'
57		#' \donttest{
58		#' ## Table with default columns.
59		#' tabulate_rsp_biomarkers(df)
60		#'
61		#' ## Table with a manually chosen set of columns: leave out "pval", reorder.
62		#' tab <- tabulate_rsp_biomarkers(
63		#' df = df,
64		#' vars = c("n_rsp", "ci", "n_tot", "prop", "or")
65		#' )
66		#'
67		#' ## Finally produce the forest plot.
68		#' g_forest(tab, xlim = c(0.7, 1.4))
69		#' }
70		#'
71		#' @export
72		#' @name response_biomarkers_subgroups
73		tabulate_rsp_biomarkers <- function(df,
74		vars = c("n_tot", "n_rsp", "prop", "or", "ci", "pval"),
75		na_str = default_na_str(),
76		.indent_mods = 0L) {
77	4x	checkmate::assert_data_frame(df)
78	4x	checkmate::assert_character(df$biomarker)
79	4x	checkmate::assert_character(df$biomarker_label)
80	4x	checkmate::assert_subset(vars, get_stats("tabulate_rsp_biomarkers"))
81
82		# Create "ci" column from "lcl" and "ucl"
83	4x	df$ci <- combine_vectors(df$lcl, df$ucl)
84
85	4x	df_subs <- split(df, f = df$biomarker)
86	4x	tabs <- lapply(df_subs, FUN = function(df_sub) {
87	7x	tab_sub <- h_tab_rsp_one_biomarker(
88	7x	df = df_sub,
89	7x	vars = vars,
90	7x	na_str = na_str,
91	7x	.indent_mods = .indent_mods
92		)
93		# Insert label row as first row in table.
94	7x	label_at_path(tab_sub, path = row_paths(tab_sub)[[1]][1]) <- df_sub$biomarker_label[1]
95	7x	tab_sub
96		})
97	4x	result <- do.call(rbind, tabs)
98
99	4x	n_id <- grep("n_tot", vars)
100	4x	or_id <- match("or", vars)
101	4x	ci_id <- match("ci", vars)
102	4x	structure(
103	4x	result,
104	4x	forest_header = paste0(c("Lower", "Higher"), "\nBetter"),
105	4x	col_x = or_id,
106	4x	col_ci = ci_id,
107	4x	col_symbol_size = n_id
108		)
109		}
110
111		#' Prepare response data estimates for multiple biomarkers in a single data frame
112		#'
113		#' @description `r lifecycle::badge("stable")`
114		#'
115		#' Prepares estimates for number of responses, patients and overall response rate,
116		#' as well as odds ratio estimates, confidence intervals and p-values,
117		#' for multiple biomarkers across population subgroups in a single data frame.
118		#' `variables` corresponds to the names of variables found in `data`, passed as a
119		#' named list and requires elements `rsp` and `biomarkers` (vector of continuous
120		#' biomarker variables) and optionally `covariates`, `subgroups` and `strata`.
121		#' `groups_lists` optionally specifies groupings for `subgroups` variables.
122		#'
123		#' @inheritParams argument_convention
124		#' @inheritParams response_subgroups
125		#' @param control (named `list`)\cr controls for the response definition and the
126		#' confidence level produced by [control_logistic()].
127		#'
128		#' @return A `data.frame` with columns `biomarker`, `biomarker_label`, `n_tot`, `n_rsp`,
129		#' `prop`, `or`, `lcl`, `ucl`, `conf_level`, `pval`, `pval_label`, `subgroup`, `var`,
130		#' `var_label`, and `row_type`.
131		#'
132		#' @note You can also specify a continuous variable in `rsp` and then use the
133		#' `response_definition` control to convert that internally to a logical
134		#' variable reflecting binary response.
135		#'
136		#' @seealso [h_logistic_mult_cont_df()] which is used internally.
137		#'
138		#' @examples
139		#' library(dplyr)
140		#' library(forcats)
141		#'
142		#' adrs <- tern_ex_adrs
143		#' adrs_labels <- formatters::var_labels(adrs)
144		#'
145		#' adrs_f <- adrs %>%
146		#' filter(PARAMCD == "BESRSPI") %>%
147		#' mutate(rsp = AVALC == "CR")
148		#'
149		#' # Typical analysis of two continuous biomarkers `BMRKR1` and `AGE`,
150		#' # in logistic regression models with one covariate `RACE`. The subgroups
151		#' # are defined by the levels of `BMRKR2`.
152		#' df <- extract_rsp_biomarkers(
153		#' variables = list(
154		#' rsp = "rsp",
155		#' biomarkers = c("BMRKR1", "AGE"),
156		#' covariates = "SEX",
157		#' subgroups = "BMRKR2"
158		#' ),
159		#' data = adrs_f
160		#' )
161		#' df
162		#'
163		#' # Here we group the levels of `BMRKR2` manually, and we add a stratification
164		#' # variable `STRATA1`. We also here use a continuous variable `EOSDY`
165		#' # which is then binarized internally (response is defined as this variable
166		#' # being larger than 750).
167		#' df_grouped <- extract_rsp_biomarkers(
168		#' variables = list(
169		#' rsp = "EOSDY",
170		#' biomarkers = c("BMRKR1", "AGE"),
171		#' covariates = "SEX",
172		#' subgroups = "BMRKR2",
173		#' strata = "STRATA1"
174		#' ),
175		#' data = adrs_f,
176		#' groups_lists = list(
177		#' BMRKR2 = list(
178		#' "low" = "LOW",
179		#' "low/medium" = c("LOW", "MEDIUM"),
180		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
181		#' )
182		#' ),
183		#' control = control_logistic(
184		#' response_definition = "I(response > 750)"
185		#' )
186		#' )
187		#' df_grouped
188		#'
189		#' @export
190		extract_rsp_biomarkers <- function(variables,
191		data,
192		groups_lists = list(),
193		control = control_logistic(),
194		label_all = "All Patients") {
195	5x	if ("strat" %in% names(variables)) {
196	!	warning(
197	!	"Warning: the `strat` element name of the `variables` list argument to `extract_rsp_biomarkers() ",
198	!	"was deprecated in tern 0.9.4.\n ",
199	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
200		)
201	!	variables[["strata"]] <- variables[["strat"]]
202		}
203
204	5x	assert_list_of_variables(variables)
205	5x	checkmate::assert_string(variables$rsp)
206	5x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
207	5x	checkmate::assert_string(label_all)
208
209		# Start with all patients.
210	5x	result_all <- h_logistic_mult_cont_df(
211	5x	variables = variables,
212	5x	data = data,
213	5x	control = control
214		)
215	5x	result_all$subgroup <- label_all
216	5x	result_all$var <- "ALL"
217	5x	result_all$var_label <- label_all
218	5x	result_all$row_type <- "content"
219	5x	if (is.null(variables$subgroups)) {
220		# Only return result for all patients.
221	1x	result_all
222		} else {
223		# Add subgroups results.
224	4x	l_data <- h_split_by_subgroups(
225	4x	data,
226	4x	variables$subgroups,
227	4x	groups_lists = groups_lists
228		)
229	4x	l_result <- lapply(l_data, function(grp) {
230	20x	result <- h_logistic_mult_cont_df(
231	20x	variables = variables,
232	20x	data = grp$df,
233	20x	control = control
234		)
235	20x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
236	20x	cbind(result, result_labels)
237		})
238	4x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
239	4x	result_subgroups$row_type <- "analysis"
240	4x	rbind(
241	4x	result_all,
242	4x	result_subgroups
243		)
244		}
245		}

1		#' Control function for Cox regression
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Sets a list of parameters for Cox regression fit. Used internally.
6		#'
7		#' @inheritParams argument_convention
8		#' @param pval_method (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
9		#' @param interaction (`flag`)\cr if `TRUE`, the model includes the interaction between the studied
10		#' treatment and candidate covariate. Note that for univariate models without treatment arm, and
11		#' multivariate models, no interaction can be used so that this needs to be `FALSE`.
12		#' @param ties (`string`)\cr among `exact` (equivalent to `DISCRETE` in SAS), `efron` and `breslow`,
13		#' see [survival::coxph()]. Note: there is no equivalent of SAS `EXACT` method in R.
14		#'
15		#' @return A `list` of items with names corresponding to the arguments.
16		#'
17		#' @seealso [fit_coxreg_univar()] and [fit_coxreg_multivar()].
18		#'
19		#' @examples
20		#' control_coxreg()
21		#'
22		#' @export
23		control_coxreg <- function(pval_method = c("wald", "likelihood"),
24		ties = c("exact", "efron", "breslow"),
25		conf_level = 0.95,
26		interaction = FALSE) {
27	55x	pval_method <- match.arg(pval_method)
28	55x	ties <- match.arg(ties)
29	55x	checkmate::assert_flag(interaction)
30	55x	assert_proportion_value(conf_level)
31	55x	list(
32	55x	pval_method = pval_method,
33	55x	ties = ties,
34	55x	conf_level = conf_level,
35	55x	interaction = interaction
36		)
37		}
38
39		#' Custom tidy methods for Cox regression
40		#'
41		#' @description `r lifecycle::badge("stable")`
42		#'
43		#' @inheritParams argument_convention
44		#' @param x (`list`)\cr result of the Cox regression model fitted by [fit_coxreg_univar()] (for univariate models)
45		#' or [fit_coxreg_multivar()] (for multivariate models).
46		#'
47		#' @return [broom::tidy()] returns:
48		#' * For `summary.coxph` objects, a `data.frame` with columns: `Pr(>\|z\|)`, `exp(coef)`, `exp(-coef)`, `lower .95`,
49		#' `upper .95`, `level`, and `n`.
50		#' * For `coxreg.univar` objects, a `data.frame` with columns: `effect`, `term`, `term_label`, `level`, `n`, `hr`,
51		#' `lcl`, `ucl`, `pval`, and `ci`.
52		#' * For `coxreg.multivar` objects, a `data.frame` with columns: `term`, `pval`, `term_label`, `hr`, `lcl`, `ucl`,
53		#' `level`, and `ci`.
54		#'
55		#' @seealso [cox_regression]
56		#'
57		#' @name tidy_coxreg
58		NULL
59
60		#' @describeIn tidy_coxreg Custom tidy method for [survival::coxph()] summary results.
61		#'
62		#' Tidy the [survival::coxph()] results into a `data.frame` to extract model results.
63		#'
64		#' @method tidy summary.coxph
65		#'
66		#' @examples
67		#' library(survival)
68		#' library(broom)
69		#'
70		#' set.seed(1, kind = "Mersenne-Twister")
71		#'
72		#' dta_bladder <- with(
73		#' data = bladder[bladder$enum < 5, ],
74		#' data.frame(
75		#' time = stop,
76		#' status = event,
77		#' armcd = as.factor(rx),
78		#' covar1 = as.factor(enum),
79		#' covar2 = factor(
80		#' sample(as.factor(enum)),
81		#' levels = 1:4, labels = c("F", "F", "M", "M")
82		#' )
83		#' )
84		#' )
85		#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
86		#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
87		#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
88		#'
89		#' formula <- "survival::Surv(time, status) ~ armcd + covar1"
90		#' msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder))
91		#' tidy(msum)
92		#'
93		#' @export
94		tidy.summary.coxph <- function(x, # nolint
95		...) {
96	199x	checkmate::assert_class(x, "summary.coxph")
97	199x	pval <- x$coefficients
98	199x	confint <- x$conf.int
99	199x	levels <- rownames(pval)
100
101	199x	pval <- tibble::as_tibble(pval)
102	199x	confint <- tibble::as_tibble(confint)
103
104	199x	ret <- cbind(pval[, grepl("Pr", names(pval))], confint)
105	199x	ret$level <- levels
106	199x	ret$n <- x[["n"]]
107	199x	ret
108		}
109
110		#' @describeIn tidy_coxreg Custom tidy method for a univariate Cox regression.
111		#'
112		#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_univar()].
113		#'
114		#' @method tidy coxreg.univar
115		#'
116		#' @examples
117		#' ## Cox regression: arm + 1 covariate.
118		#' mod1 <- fit_coxreg_univar(
119		#' variables = list(
120		#' time = "time", event = "status", arm = "armcd",
121		#' covariates = "covar1"
122		#' ),
123		#' data = dta_bladder,
124		#' control = control_coxreg(conf_level = 0.91)
125		#' )
126		#'
127		#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
128		#' mod2 <- fit_coxreg_univar(
129		#' variables = list(
130		#' time = "time", event = "status", arm = "armcd",
131		#' covariates = c("covar1", "covar2")
132		#' ),
133		#' data = dta_bladder,
134		#' control = control_coxreg(conf_level = 0.91, interaction = TRUE)
135		#' )
136		#'
137		#' tidy(mod1)
138		#' tidy(mod2)
139		#'
140		#' @export
141		tidy.coxreg.univar <- function(x, # nolint
142		...) {
143	38x	checkmate::assert_class(x, "coxreg.univar")
144	38x	mod <- x$mod
145	38x	vars <- c(x$vars$arm, x$vars$covariates)
146	38x	has_arm <- "arm" %in% names(x$vars)
147
148	38x	result <- if (!has_arm) {
149	5x	Map(
150	5x	mod = mod, vars = vars,
151	5x	f = function(mod, vars) {
152	6x	h_coxreg_multivar_extract(
153	6x	var = vars,
154	6x	data = x$data,
155	6x	mod = mod,
156	6x	control = x$control
157		)
158		}
159		)
160	38x	} else if (x$control$interaction) {
161	12x	Map(
162	12x	mod = mod, covar = vars,
163	12x	f = function(mod, covar) {
164	26x	h_coxreg_extract_interaction(
165	26x	effect = x$vars$arm, covar = covar, mod = mod, data = x$data,
166	26x	at = x$at, control = x$control
167		)
168		}
169		)
170		} else {
171	21x	Map(
172	21x	mod = mod, vars = vars,
173	21x	f = function(mod, vars) {
174	53x	h_coxreg_univar_extract(
175	53x	effect = x$vars$arm, covar = vars, data = x$data, mod = mod,
176	53x	control = x$control
177		)
178		}
179		)
180		}
181	38x	result <- do.call(rbind, result)
182
183	38x	result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
184	38x	result$n <- lapply(result$n, empty_vector_if_na)
185	38x	result$ci <- lapply(result$ci, empty_vector_if_na)
186	38x	result$hr <- lapply(result$hr, empty_vector_if_na)
187	38x	if (x$control$interaction) {
188	12x	result$pval_inter <- lapply(result$pval_inter, empty_vector_if_na)
189		# Remove interaction p-values due to change in specifications.
190	12x	result$pval[result$effect != "Treatment:"] <- NA
191		}
192	38x	result$pval <- lapply(result$pval, empty_vector_if_na)
193	38x	attr(result, "conf_level") <- x$control$conf_level
194	38x	result
195		}
196
197		#' @describeIn tidy_coxreg Custom tidy method for a multivariate Cox regression.
198		#'
199		#' Tidy up the result of a Cox regression model fitted by [fit_coxreg_multivar()].
200		#'
201		#' @method tidy coxreg.multivar
202		#'
203		#' @examples
204		#' multivar_model <- fit_coxreg_multivar(
205		#' variables = list(
206		#' time = "time", event = "status", arm = "armcd",
207		#' covariates = c("covar1", "covar2")
208		#' ),
209		#' data = dta_bladder
210		#' )
211		#' broom::tidy(multivar_model)
212		#'
213		#' @export
214		tidy.coxreg.multivar <- function(x, # nolint
215		...) {
216	16x	checkmate::assert_class(x, "coxreg.multivar")
217	16x	vars <- c(x$vars$arm, x$vars$covariates)
218
219		# Convert the model summaries to data.
220	16x	result <- Map(
221	16x	vars = vars,
222	16x	f = function(vars) {
223	60x	h_coxreg_multivar_extract(
224	60x	var = vars, data = x$data,
225	60x	mod = x$mod, control = x$control
226		)
227		}
228		)
229	16x	result <- do.call(rbind, result)
230
231	16x	result$ci <- Map(lcl = result$lcl, ucl = result$ucl, f = function(lcl, ucl) c(lcl, ucl))
232	16x	result$ci <- lapply(result$ci, empty_vector_if_na)
233	16x	result$hr <- lapply(result$hr, empty_vector_if_na)
234	16x	result$pval <- lapply(result$pval, empty_vector_if_na)
235	16x	result <- result[, names(result) != "n"]
236	16x	attr(result, "conf_level") <- x$control$conf_level
237
238	16x	result
239		}
240
241		#' Fitting functions for Cox proportional hazards regression
242		#'
243		#' @description `r lifecycle::badge("stable")`
244		#'
245		#' Fitting functions for univariate and multivariate Cox regression models.
246		#'
247		#' @param variables (named `list`)\cr the names of the variables found in `data`, passed as a named list and
248		#' corresponding to the `time`, `event`, `arm`, `strata`, and `covariates` terms. If `arm` is missing from
249		#' `variables`, then only Cox model(s) including the `covariates` will be fitted and the corresponding effect
250		#' estimates will be tabulated later.
251		#' @param data (`data.frame`)\cr the dataset containing the variables to fit the models.
252		#' @param at (`list` of `numeric`)\cr when the candidate covariate is a `numeric`, use `at` to specify
253		#' the value of the covariate at which the effect should be estimated.
254		#' @param control (`list`)\cr a list of parameters as returned by the helper function [control_coxreg()].
255		#'
256		#' @seealso [h_cox_regression] for relevant helper functions, [cox_regression].
257		#'
258		#' @examples
259		#' library(survival)
260		#'
261		#' set.seed(1, kind = "Mersenne-Twister")
262		#'
263		#' # Testing dataset [survival::bladder].
264		#' dta_bladder <- with(
265		#' data = bladder[bladder$enum < 5, ],
266		#' data.frame(
267		#' time = stop,
268		#' status = event,
269		#' armcd = as.factor(rx),
270		#' covar1 = as.factor(enum),
271		#' covar2 = factor(
272		#' sample(as.factor(enum)),
273		#' levels = 1:4, labels = c("F", "F", "M", "M")
274		#' )
275		#' )
276		#' )
277		#' labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
278		#' formatters::var_labels(dta_bladder)[names(labels)] <- labels
279		#' dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)
280		#'
281		#' plot(
282		#' survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
283		#' lty = 2:4,
284		#' xlab = "Months",
285		#' col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
286		#' )
287		#'
288		#' @name fit_coxreg
289		NULL
290
291		#' @describeIn fit_coxreg Fit a series of univariate Cox regression models given the inputs.
292		#'
293		#' @return
294		#' * `fit_coxreg_univar()` returns a `coxreg.univar` class object which is a named `list`
295		#' with 5 elements:
296		#' * `mod`: Cox regression models fitted by [survival::coxph()].
297		#' * `data`: The original data frame input.
298		#' * `control`: The original control input.
299		#' * `vars`: The variables used in the model.
300		#' * `at`: Value of the covariate at which the effect should be estimated.
301		#'
302		#' @note When using `fit_coxreg_univar` there should be two study arms.
303		#'
304		#' @examples
305		#' # fit_coxreg_univar
306		#'
307		#' ## Cox regression: arm + 1 covariate.
308		#' mod1 <- fit_coxreg_univar(
309		#' variables = list(
310		#' time = "time", event = "status", arm = "armcd",
311		#' covariates = "covar1"
312		#' ),
313		#' data = dta_bladder,
314		#' control = control_coxreg(conf_level = 0.91)
315		#' )
316		#'
317		#' ## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
318		#' mod2 <- fit_coxreg_univar(
319		#' variables = list(
320		#' time = "time", event = "status", arm = "armcd",
321		#' covariates = c("covar1", "covar2")
322		#' ),
323		#' data = dta_bladder,
324		#' control = control_coxreg(conf_level = 0.91, interaction = TRUE)
325		#' )
326		#'
327		#' ## Cox regression: arm + 1 covariate, stratified analysis.
328		#' mod3 <- fit_coxreg_univar(
329		#' variables = list(
330		#' time = "time", event = "status", arm = "armcd", strata = "covar2",
331		#' covariates = c("covar1")
332		#' ),
333		#' data = dta_bladder,
334		#' control = control_coxreg(conf_level = 0.91)
335		#' )
336		#'
337		#' ## Cox regression: no arm, only covariates.
338		#' mod4 <- fit_coxreg_univar(
339		#' variables = list(
340		#' time = "time", event = "status",
341		#' covariates = c("covar1", "covar2")
342		#' ),
343		#' data = dta_bladder
344		#' )
345		#'
346		#' @export
347		fit_coxreg_univar <- function(variables,
348		data,
349		at = list(),
350		control = control_coxreg()) {
351	43x	checkmate::assert_list(variables, names = "named")
352	43x	has_arm <- "arm" %in% names(variables)
353	43x	arm_name <- if (has_arm) "arm" else NULL
354
355	43x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
356
357	43x	assert_df_with_variables(data, variables)
358	43x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
359
360	43x	if (!is.null(variables$strata)) {
361	4x	checkmate::assert_disjunct(control$pval_method, "likelihood")
362		}
363	42x	if (has_arm) {
364	36x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
365		}
366	41x	vars <- unlist(variables[c(arm_name, "covariates", "strata")], use.names = FALSE)
367	41x	for (i in vars) {
368	94x	if (is.factor(data[[i]])) {
369	82x	attr(data[[i]], "levels") <- levels(droplevels(data[[i]]))
370		}
371		}
372	41x	forms <- h_coxreg_univar_formulas(variables, interaction = control$interaction)
373	41x	mod <- lapply(
374	41x	forms, function(x) {
375	90x	survival::coxph(formula = stats::as.formula(x), data = data, ties = control$ties)
376		}
377		)
378	41x	structure(
379	41x	list(
380	41x	mod = mod,
381	41x	data = data,
382	41x	control = control,
383	41x	vars = variables,
384	41x	at = at
385		),
386	41x	class = "coxreg.univar"
387		)
388		}
389
390		#' @describeIn fit_coxreg Fit a multivariate Cox regression model.
391		#'
392		#' @return
393		#' * `fit_coxreg_multivar()` returns a `coxreg.multivar` class object which is a named list
394		#' with 4 elements:
395		#' * `mod`: Cox regression model fitted by [survival::coxph()].
396		#' * `data`: The original data frame input.
397		#' * `control`: The original control input.
398		#' * `vars`: The variables used in the model.
399		#'
400		#' @examples
401		#' # fit_coxreg_multivar
402		#'
403		#' ## Cox regression: multivariate Cox regression.
404		#' multivar_model <- fit_coxreg_multivar(
405		#' variables = list(
406		#' time = "time", event = "status", arm = "armcd",
407		#' covariates = c("covar1", "covar2")
408		#' ),
409		#' data = dta_bladder
410		#' )
411		#'
412		#' # Example without treatment arm.
413		#' multivar_covs_model <- fit_coxreg_multivar(
414		#' variables = list(
415		#' time = "time", event = "status",
416		#' covariates = c("covar1", "covar2")
417		#' ),
418		#' data = dta_bladder
419		#' )
420		#'
421		#' @export
422		fit_coxreg_multivar <- function(variables,
423		data,
424		control = control_coxreg()) {
425	83x	checkmate::assert_list(variables, names = "named")
426	83x	has_arm <- "arm" %in% names(variables)
427	83x	arm_name <- if (has_arm) "arm" else NULL
428
429	83x	if (!is.null(variables$covariates)) {
430	21x	checkmate::assert_character(variables$covariates)
431		}
432
433	83x	checkmate::assert_false(control$interaction)
434	83x	assert_df_with_variables(data, variables)
435	83x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
436
437	83x	if (!is.null(variables$strata)) {
438	3x	checkmate::assert_disjunct(control$pval_method, "likelihood")
439		}
440
441	82x	form <- h_coxreg_multivar_formula(variables)
442	82x	mod <- survival::coxph(
443	82x	formula = stats::as.formula(form),
444	82x	data = data,
445	82x	ties = control$ties
446		)
447	82x	structure(
448	82x	list(
449	82x	mod = mod,
450	82x	data = data,
451	82x	control = control,
452	82x	vars = variables
453		),
454	82x	class = "coxreg.multivar"
455		)
456		}
457
458		#' Muffled `car::Anova`
459		#'
460		#' Applied on survival models, [car::Anova()] signal that the `strata` terms is dropped from the model formula when
461		#' present, this function deliberately muffles this message.
462		#'
463		#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
464		#' @param test_statistic (`string`)\cr the method used for estimation of p.values; `wald` (default) or `likelihood`.
465		#'
466		#' @return The output of [car::Anova()], with convergence message muffled.
467		#'
468		#' @keywords internal
469		muffled_car_anova <- function(mod, test_statistic) {
470	219x	tryCatch(
471	219x	withCallingHandlers(
472	219x	expr = {
473	219x	car::Anova(
474	219x	mod,
475	219x	test.statistic = test_statistic,
476	219x	type = "III"
477		)
478		},
479	219x	message = function(m) invokeRestart("muffleMessage"),
480	219x	error = function(e) {
481	1x	stop(paste(
482	1x	"the model seems to have convergence problems, please try to change",
483	1x	"the configuration of covariates or strata variables, e.g.",
484	1x	"- original error:", e
485		))
486		}
487		)
488		)
489		}

1		#' Confidence interval for mean
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Convenient function for calculating the mean confidence interval. It calculates the arithmetic as well as the
6		#' geometric mean. It can be used as a `ggplot` helper function for plotting.
7		#'
8		#' @inheritParams argument_convention
9		#' @param n_min (`numeric(1)`)\cr a minimum number of non-missing `x` to estimate the confidence interval for mean.
10		#' @param gg_helper (`flag`)\cr whether output should be aligned for use with `ggplot`s.
11		#' @param geom_mean (`flag`)\cr whether the geometric mean should be calculated.
12		#'
13		#' @return A named `vector` of values `mean_ci_lwr` and `mean_ci_upr`.
14		#'
15		#' @examples
16		#' stat_mean_ci(sample(10), gg_helper = FALSE)
17		#'
18		#' p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +
19		#' ggplot2::geom_point()
20		#'
21		#' p + ggplot2::stat_summary(
22		#' fun.data = stat_mean_ci,
23		#' geom = "errorbar"
24		#' )
25		#'
26		#' p + ggplot2::stat_summary(
27		#' fun.data = stat_mean_ci,
28		#' fun.args = list(conf_level = 0.5),
29		#' geom = "errorbar"
30		#' )
31		#'
32		#' p + ggplot2::stat_summary(
33		#' fun.data = stat_mean_ci,
34		#' fun.args = list(conf_level = 0.5, geom_mean = TRUE),
35		#' geom = "errorbar"
36		#' )
37		#'
38		#' @export
39		stat_mean_ci <- function(x,
40		conf_level = 0.95,
41		na.rm = TRUE, # nolint
42		n_min = 2,
43		gg_helper = TRUE,
44		geom_mean = FALSE) {
45	2295x	if (na.rm) {
46	10x	x <- stats::na.omit(x)
47		}
48	2295x	n <- length(x)
49
50	2295x	if (!geom_mean) {
51	1155x	m <- mean(x)
52		} else {
53	1140x	negative_values_exist <- any(is.na(x[!is.na(x)]) <- x[!is.na(x)] <= 0)
54	1140x	if (negative_values_exist) {
55	24x	m <- NA_real_
56		} else {
57	1116x	x <- log(x)
58	1116x	m <- mean(x)
59		}
60		}
61
62	2295x	if (n < n_min \|\| is.na(m)) {
63	304x	ci <- c(mean_ci_lwr = NA_real_, mean_ci_upr = NA_real_)
64		} else {
65	1991x	hci <- stats::qt((1 + conf_level) / 2, df = n - 1) * stats::sd(x) / sqrt(n)
66	1991x	ci <- c(mean_ci_lwr = m - hci, mean_ci_upr = m + hci)
67	1991x	if (geom_mean) {
68	985x	ci <- exp(ci)
69		}
70		}
71
72	2295x	if (gg_helper) {
73	4x	m <- ifelse(is.na(m), NA_real_, m)
74	4x	ci <- data.frame(y = ifelse(geom_mean, exp(m), m), ymin = ci[[1]], ymax = ci[[2]])
75		}
76
77	2295x	return(ci)
78		}
79
80		#' Confidence interval for median
81		#'
82		#' @description `r lifecycle::badge("stable")`
83		#'
84		#' Convenient function for calculating the median confidence interval. It can be used as a `ggplot` helper
85		#' function for plotting.
86		#'
87		#' @inheritParams argument_convention
88		#' @param gg_helper (`flag`)\cr whether output should be aligned for use with `ggplot`s.
89		#'
90		#' @details This function was adapted from `DescTools/versions/0.99.35/source`
91		#'
92		#' @return A named `vector` of values `median_ci_lwr` and `median_ci_upr`.
93		#'
94		#' @examples
95		#' stat_median_ci(sample(10), gg_helper = FALSE)
96		#'
97		#' p <- ggplot2::ggplot(mtcars, ggplot2::aes(cyl, mpg)) +
98		#' ggplot2::geom_point()
99		#' p + ggplot2::stat_summary(
100		#' fun.data = stat_median_ci,
101		#' geom = "errorbar"
102		#' )
103		#'
104		#' @export
105		stat_median_ci <- function(x,
106		conf_level = 0.95,
107		na.rm = TRUE, # nolint
108		gg_helper = TRUE) {
109	1153x	x <- unname(x)
110	1153x	if (na.rm) {
111	9x	x <- x[!is.na(x)]
112		}
113	1153x	n <- length(x)
114	1153x	med <- stats::median(x)
115
116	1153x	k <- stats::qbinom(p = (1 - conf_level) / 2, size = n, prob = 0.5, lower.tail = TRUE)
117
118		# k == 0 - for small samples (e.g. n <= 5) ci can be outside the observed range
119	1153x	if (k == 0 \|\| is.na(med)) {
120	248x	ci <- c(median_ci_lwr = NA_real_, median_ci_upr = NA_real_)
121	248x	empir_conf_level <- NA_real_
122		} else {
123	905x	x_sort <- sort(x)
124	905x	ci <- c(median_ci_lwr = x_sort[k], median_ci_upr = x_sort[n - k + 1])
125	905x	empir_conf_level <- 1 - 2 * stats::pbinom(k - 1, size = n, prob = 0.5)
126		}
127
128	1153x	if (gg_helper) {
129	4x	ci <- data.frame(y = med, ymin = ci[[1]], ymax = ci[[2]])
130		}
131
132	1153x	attr(ci, "conf_level") <- empir_conf_level
133
134	1153x	return(ci)
135		}
136
137		#' p-Value of the mean
138		#'
139		#' @description `r lifecycle::badge("stable")`
140		#'
141		#' Convenient function for calculating the two-sided p-value of the mean.
142		#'
143		#' @inheritParams argument_convention
144		#' @param n_min (`numeric(1)`)\cr a minimum number of non-missing `x` to estimate the p-value of the mean.
145		#' @param test_mean (`numeric(1)`)\cr mean value to test under the null hypothesis.
146		#'
147		#' @return A p-value.
148		#'
149		#' @examples
150		#' stat_mean_pval(sample(10))
151		#'
152		#' stat_mean_pval(rnorm(10), test_mean = 0.5)
153		#'
154		#' @export
155		stat_mean_pval <- function(x,
156		na.rm = TRUE, # nolint
157		n_min = 2,
158		test_mean = 0) {
159	1153x	if (na.rm) {
160	9x	x <- stats::na.omit(x)
161		}
162	1153x	n <- length(x)
163
164	1153x	x_mean <- mean(x)
165	1153x	x_sd <- stats::sd(x)
166
167	1153x	if (n < n_min) {
168	140x	pv <- c(p_value = NA_real_)
169		} else {
170	1013x	x_se <- stats::sd(x) / sqrt(n)
171	1013x	ttest <- (x_mean - test_mean) / x_se
172	1013x	pv <- c(p_value = 2 * stats::pt(-abs(ttest), df = n - 1))
173		}
174
175	1153x	return(pv)
176		}
177
178		#' Proportion difference and confidence interval
179		#'
180		#' @description `r lifecycle::badge("stable")`
181		#'
182		#' Function for calculating the proportion (or risk) difference and confidence interval between arm
183		#' X (reference group) and arm Y. Risk difference is calculated by subtracting cumulative incidence
184		#' in arm Y from cumulative incidence in arm X.
185		#'
186		#' @inheritParams argument_convention
187		#' @param x (`list` of `integer`)\cr list of number of occurrences in arm X (reference group).
188		#' @param y (`list` of `integer`)\cr list of number of occurrences in arm Y. Must be of equal length to `x`.
189		#' @param N_x (`numeric(1)`)\cr total number of records in arm X.
190		#' @param N_y (`numeric(1)`)\cr total number of records in arm Y.
191		#' @param list_names (`character`)\cr names of each variable/level corresponding to pair of proportions in
192		#' `x` and `y`. Must be of equal length to `x` and `y`.
193		#' @param pct (`flag`)\cr whether output should be returned as percentages. Defaults to `TRUE`.
194		#'
195		#' @return List of proportion differences and CIs corresponding to each pair of number of occurrences in `x` and
196		#' `y`. Each list element consists of 3 statistics: proportion difference, CI lower bound, and CI upper bound.
197		#'
198		#' @seealso Split function [add_riskdiff()] which, when used as `split_fun` within [rtables::split_cols_by()]
199		#' with `riskdiff` argument is set to `TRUE` in subsequent analyze functions, adds a column containing
200		#' proportion (risk) difference to an `rtables` layout.
201		#'
202		#' @examples
203		#' stat_propdiff_ci(
204		#' x = list(0.375), y = list(0.01), N_x = 5, N_y = 5, list_names = "x", conf_level = 0.9
205		#' )
206		#'
207		#' stat_propdiff_ci(
208		#' x = list(0.5, 0.75, 1), y = list(0.25, 0.05, 0.5), N_x = 10, N_y = 20, pct = FALSE
209		#' )
210		#'
211		#' @export
212		stat_propdiff_ci <- function(x,
213		y,
214		N_x, # nolint
215		N_y, # nolint
216		list_names = NULL,
217		conf_level = 0.95,
218		pct = TRUE) {
219	51x	checkmate::assert_list(x, types = "numeric")
220	51x	checkmate::assert_list(y, types = "numeric", len = length(x))
221	51x	checkmate::assert_character(list_names, len = length(x), null.ok = TRUE)
222	51x	rd_list <- lapply(seq_along(x), function(i) {
223	134x	p_x <- x[[i]] / N_x
224	134x	p_y <- y[[i]] / N_y
225	134x	rd_ci <- p_x - p_y + c(-1, 1) * stats::qnorm((1 + conf_level) / 2) *
226	134x	sqrt(p_x * (1 - p_x) / N_x + p_y * (1 - p_y) / N_y)
227	134x	c(p_x - p_y, rd_ci) * ifelse(pct, 100, 1)
228		})
229	51x	names(rd_list) <- list_names
230	51x	rd_list
231		}

1		#' Helper functions for Cox proportional hazards regression
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions used in [fit_coxreg_univar()] and [fit_coxreg_multivar()].
6		#'
7		#' @inheritParams argument_convention
8		#' @inheritParams h_coxreg_univar_extract
9		#' @inheritParams cox_regression_inter
10		#' @inheritParams control_coxreg
11		#'
12		#' @seealso [cox_regression]
13		#'
14		#' @name h_cox_regression
15		NULL
16
17		#' @describeIn h_cox_regression Helper for Cox regression formula. Creates a list of formulas. It is used
18		#' internally by [fit_coxreg_univar()] for the comparison of univariate Cox regression models.
19		#'
20		#' @return
21		#' * `h_coxreg_univar_formulas()` returns a `character` vector coercible into formulas (e.g [stats::as.formula()]).
22		#'
23		#' @examples
24		#' # `h_coxreg_univar_formulas`
25		#'
26		#' ## Simple formulas.
27		#' h_coxreg_univar_formulas(
28		#' variables = list(
29		#' time = "time", event = "status", arm = "armcd", covariates = c("X", "y")
30		#' )
31		#' )
32		#'
33		#' ## Addition of an optional strata.
34		#' h_coxreg_univar_formulas(
35		#' variables = list(
36		#' time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
37		#' strata = "SITE"
38		#' )
39		#' )
40		#'
41		#' ## Inclusion of the interaction term.
42		#' h_coxreg_univar_formulas(
43		#' variables = list(
44		#' time = "time", event = "status", arm = "armcd", covariates = c("X", "y"),
45		#' strata = "SITE"
46		#' ),
47		#' interaction = TRUE
48		#' )
49		#'
50		#' ## Only covariates fitted in separate models.
51		#' h_coxreg_univar_formulas(
52		#' variables = list(
53		#' time = "time", event = "status", covariates = c("X", "y")
54		#' )
55		#' )
56		#'
57		#' @export
58		h_coxreg_univar_formulas <- function(variables,
59		interaction = FALSE) {
60	50x	checkmate::assert_list(variables, names = "named")
61	50x	has_arm <- "arm" %in% names(variables)
62	50x	arm_name <- if (has_arm) "arm" else NULL
63
64	50x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
65
66	50x	checkmate::assert_flag(interaction)
67
68	50x	if (!has_arm \|\| is.null(variables$covariates)) {
69	10x	checkmate::assert_false(interaction)
70		}
71
72	48x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
73
74	48x	if (!is.null(variables$covariates)) {
75	47x	forms <- paste0(
76	47x	"survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
77	47x	ifelse(has_arm, variables$arm, "1"),
78	47x	ifelse(interaction, " * ", " + "),
79	47x	variables$covariates,
80	47x	ifelse(
81	47x	!is.null(variables$strata),
82	47x	paste0(" + strata(", paste0(variables$strata, collapse = ", "), ")"),
83		""
84		)
85		)
86		} else {
87	1x	forms <- NULL
88		}
89	48x	nams <- variables$covariates
90	48x	if (has_arm) {
91	41x	ref <- paste0(
92	41x	"survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
93	41x	variables$arm,
94	41x	ifelse(
95	41x	!is.null(variables$strata),
96	41x	paste0(
97	41x	" + strata(", paste0(variables$strata, collapse = ", "), ")"
98		),
99		""
100		)
101		)
102	41x	forms <- c(ref, forms)
103	41x	nams <- c("ref", nams)
104		}
105	48x	stats::setNames(forms, nams)
106		}
107
108		#' @describeIn h_cox_regression Helper for multivariate Cox regression formula. Creates a formulas
109		#' string. It is used internally by [fit_coxreg_multivar()] for the comparison of multivariate Cox
110		#' regression models. Interactions will not be included in multivariate Cox regression model.
111		#'
112		#' @return
113		#' * `h_coxreg_multivar_formula()` returns a `string` coercible into a formula (e.g [stats::as.formula()]).
114		#'
115		#' @examples
116		#' # `h_coxreg_multivar_formula`
117		#'
118		#' h_coxreg_multivar_formula(
119		#' variables = list(
120		#' time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE")
121		#' )
122		#' )
123		#'
124		#' # Addition of an optional strata.
125		#' h_coxreg_multivar_formula(
126		#' variables = list(
127		#' time = "AVAL", event = "event", arm = "ARMCD", covariates = c("RACE", "AGE"),
128		#' strata = "SITE"
129		#' )
130		#' )
131		#'
132		#' # Example without treatment arm.
133		#' h_coxreg_multivar_formula(
134		#' variables = list(
135		#' time = "AVAL", event = "event", covariates = c("RACE", "AGE"),
136		#' strata = "SITE"
137		#' )
138		#' )
139		#'
140		#' @export
141		h_coxreg_multivar_formula <- function(variables) {
142	89x	checkmate::assert_list(variables, names = "named")
143	89x	has_arm <- "arm" %in% names(variables)
144	89x	arm_name <- if (has_arm) "arm" else NULL
145
146	89x	checkmate::assert_character(variables$covariates, null.ok = TRUE)
147
148	89x	assert_list_of_variables(variables[c(arm_name, "event", "time")])
149
150	89x	y <- paste0(
151	89x	"survival::Surv(", variables$time, ", ", variables$event, ") ~ ",
152	89x	ifelse(has_arm, variables$arm, "1")
153		)
154	89x	if (length(variables$covariates) > 0) {
155	26x	y <- paste(y, paste(variables$covariates, collapse = " + "), sep = " + ")
156		}
157	89x	if (!is.null(variables$strata)) {
158	5x	y <- paste0(y, " + strata(", paste0(variables$strata, collapse = ", "), ")")
159		}
160	89x	y
161		}
162
163		#' @describeIn h_cox_regression Utility function to help tabulate the result of
164		#' a univariate Cox regression model.
165		#'
166		#' @param effect (`string`)\cr the treatment variable.
167		#' @param mod (`coxph`)\cr Cox regression model fitted by [survival::coxph()].
168		#'
169		#' @return
170		#' * `h_coxreg_univar_extract()` returns a `data.frame` with variables `effect`, `term`, `term_label`, `level`,
171		#' `n`, `hr`, `lcl`, `ucl`, and `pval`.
172		#'
173		#' @examples
174		#' library(survival)
175		#'
176		#' dta_simple <- data.frame(
177		#' time = c(5, 5, 10, 10, 5, 5, 10, 10),
178		#' status = c(0, 0, 1, 0, 0, 1, 1, 1),
179		#' armcd = factor(LETTERS[c(1, 1, 1, 1, 2, 2, 2, 2)], levels = c("A", "B")),
180		#' var1 = c(45, 55, 65, 75, 55, 65, 85, 75),
181		#' var2 = c("F", "M", "F", "M", "F", "M", "F", "U")
182		#' )
183		#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
184		#' result <- h_coxreg_univar_extract(
185		#' effect = "armcd", covar = "armcd", mod = mod, data = dta_simple
186		#' )
187		#' result
188		#'
189		#' @export
190		h_coxreg_univar_extract <- function(effect,
191		covar,
192		data,
193		mod,
194		control = control_coxreg()) {
195	66x	checkmate::assert_string(covar)
196	66x	checkmate::assert_string(effect)
197	66x	checkmate::assert_class(mod, "coxph")
198	66x	test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
199
200	66x	mod_aov <- muffled_car_anova(mod, test_statistic)
201	66x	msum <- summary(mod, conf.int = control$conf_level)
202	66x	sum_cox <- broom::tidy(msum)
203
204		# Combine results together.
205	66x	effect_aov <- mod_aov[effect, , drop = TRUE]
206	66x	pval <- effect_aov[[grep(pattern = "Pr", x = names(effect_aov)), drop = TRUE]]
207	66x	sum_main <- sum_cox[grepl(effect, sum_cox$level), ]
208
209	66x	term_label <- if (effect == covar) {
210	34x	paste0(
211	34x	levels(data[[covar]])[2],
212	34x	" vs control (",
213	34x	levels(data[[covar]])[1],
214		")"
215		)
216		} else {
217	32x	unname(labels_or_names(data[covar]))
218		}
219	66x	data.frame(
220	66x	effect = ifelse(covar == effect, "Treatment:", "Covariate:"),
221	66x	term = covar,
222	66x	term_label = term_label,
223	66x	level = levels(data[[effect]])[2],
224	66x	n = mod[["n"]],
225	66x	hr = unname(sum_main["exp(coef)"]),
226	66x	lcl = unname(sum_main[grep("lower", names(sum_main))]),
227	66x	ucl = unname(sum_main[grep("upper", names(sum_main))]),
228	66x	pval = pval,
229	66x	stringsAsFactors = FALSE
230		)
231		}
232
233		#' @describeIn h_cox_regression Tabulation of multivariate Cox regressions. Utility function to help
234		#' tabulate the result of a multivariate Cox regression model for a treatment/covariate variable.
235		#'
236		#' @return
237		#' * `h_coxreg_multivar_extract()` returns a `data.frame` with variables `pval`, `hr`, `lcl`, `ucl`, `level`,
238		#' `n`, `term`, and `term_label`.
239		#'
240		#' @examples
241		#' mod <- coxph(Surv(time, status) ~ armcd + var1, data = dta_simple)
242		#' result <- h_coxreg_multivar_extract(
243		#' var = "var1", mod = mod, data = dta_simple
244		#' )
245		#' result
246		#'
247		#' @export
248		h_coxreg_multivar_extract <- function(var,
249		data,
250		mod,
251		control = control_coxreg()) {
252	132x	test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
253	132x	mod_aov <- muffled_car_anova(mod, test_statistic)
254
255	132x	msum <- summary(mod, conf.int = control$conf_level)
256	132x	sum_anova <- broom::tidy(mod_aov)
257	132x	sum_cox <- broom::tidy(msum)
258
259	132x	ret_anova <- sum_anova[sum_anova$term == var, c("term", "p.value")]
260	132x	names(ret_anova)[2] <- "pval"
261	132x	if (is.factor(data[[var]])) {
262	53x	ret_cox <- sum_cox[startsWith(prefix = var, x = sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
263		} else {
264	79x	ret_cox <- sum_cox[(var == sum_cox$level), !(names(sum_cox) %in% "exp(-coef)")]
265		}
266	132x	names(ret_cox)[1:4] <- c("pval", "hr", "lcl", "ucl")
267	132x	varlab <- unname(labels_or_names(data[var]))
268	132x	ret_cox$term <- varlab
269
270	132x	if (is.numeric(data[[var]])) {
271	79x	ret <- ret_cox
272	79x	ret$term_label <- ret$term
273	53x	} else if (length(levels(data[[var]])) <= 2) {
274	34x	ret_anova$pval <- NA
275	34x	ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
276	34x	ret_cox$level <- gsub(var, "", ret_cox$level)
277	34x	ret_cox$term_label <- ret_cox$level
278	34x	ret <- dplyr::bind_rows(ret_anova, ret_cox)
279		} else {
280	19x	ret_anova$term_label <- paste0(varlab, " (reference = ", levels(data[[var]])[1], ")")
281	19x	ret_cox$level <- gsub(var, "", ret_cox$level)
282	19x	ret_cox$term_label <- ret_cox$level
283	19x	ret <- dplyr::bind_rows(ret_anova, ret_cox)
284		}
285
286	132x	as.data.frame(ret)
287		}

1		#' Helper functions for tabulating binary response by subgroup
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Helper functions that tabulate in a data frame statistics such as response rate
6		#' and odds ratio for population subgroups.
7		#'
8		#' @inheritParams argument_convention
9		#' @inheritParams response_subgroups
10		#' @param arm (`factor`)\cr the treatment group variable.
11		#'
12		#' @details Main functionality is to prepare data for use in a layout-creating function.
13		#'
14		#' @examples
15		#' library(dplyr)
16		#' library(forcats)
17		#'
18		#' adrs <- tern_ex_adrs
19		#' adrs_labels <- formatters::var_labels(adrs)
20		#'
21		#' adrs_f <- adrs %>%
22		#' filter(PARAMCD == "BESRSPI") %>%
23		#' filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
24		#' droplevels() %>%
25		#' mutate(
26		#' # Reorder levels of factor to make the placebo group the reference arm.
27		#' ARM = fct_relevel(ARM, "B: Placebo"),
28		#' rsp = AVALC == "CR"
29		#' )
30		#' formatters::var_labels(adrs_f) <- c(adrs_labels, "Response")
31		#'
32		#' @name h_response_subgroups
33		NULL
34
35		#' @describeIn h_response_subgroups Helper to prepare a data frame of binary responses by arm.
36		#'
37		#' @return
38		#' * `h_proportion_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, and `prop`.
39		#'
40		#' @examples
41		#' h_proportion_df(
42		#' c(TRUE, FALSE, FALSE),
43		#' arm = factor(c("A", "A", "B"), levels = c("A", "B"))
44		#' )
45		#'
46		#' @export
47		h_proportion_df <- function(rsp, arm) {
48	79x	checkmate::assert_logical(rsp)
49	78x	assert_valid_factor(arm, len = length(rsp))
50	78x	non_missing_rsp <- !is.na(rsp)
51	78x	rsp <- rsp[non_missing_rsp]
52	78x	arm <- arm[non_missing_rsp]
53
54	78x	lst_rsp <- split(rsp, arm)
55	78x	lst_results <- Map(function(x, arm) {
56	156x	if (length(x) > 0) {
57	154x	s_prop <- s_proportion(df = x)
58	154x	data.frame(
59	154x	arm = arm,
60	154x	n = length(x),
61	154x	n_rsp = unname(s_prop$n_prop[1]),
62	154x	prop = unname(s_prop$n_prop[2]),
63	154x	stringsAsFactors = FALSE
64		)
65		} else {
66	2x	data.frame(
67	2x	arm = arm,
68	2x	n = 0L,
69	2x	n_rsp = NA,
70	2x	prop = NA,
71	2x	stringsAsFactors = FALSE
72		)
73		}
74	78x	}, lst_rsp, names(lst_rsp))
75
76	78x	df <- do.call(rbind, args = c(lst_results, make.row.names = FALSE))
77	78x	df$arm <- factor(df$arm, levels = levels(arm))
78	78x	df
79		}
80
81		#' @describeIn h_response_subgroups Summarizes proportion of binary responses by arm and across subgroups
82		#' in a data frame. `variables` corresponds to the names of variables found in `data`, passed as a named list and
83		#' requires elements `rsp`, `arm` and optionally `subgroups`. `groups_lists` optionally specifies
84		#' groupings for `subgroups` variables.
85		#'
86		#' @return
87		#' * `h_proportion_subgroups_df()` returns a `data.frame` with columns `arm`, `n`, `n_rsp`, `prop`, `subgroup`,
88		#' `var`, `var_label`, and `row_type`.
89		#'
90		#' @examples
91		#' h_proportion_subgroups_df(
92		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
93		#' data = adrs_f
94		#' )
95		#'
96		#' # Define groupings for BMRKR2 levels.
97		#' h_proportion_subgroups_df(
98		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
99		#' data = adrs_f,
100		#' groups_lists = list(
101		#' BMRKR2 = list(
102		#' "low" = "LOW",
103		#' "low/medium" = c("LOW", "MEDIUM"),
104		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
105		#' )
106		#' )
107		#' )
108		#'
109		#' @export
110		h_proportion_subgroups_df <- function(variables,
111		data,
112		groups_lists = list(),
113		label_all = "All Patients") {
114	17x	checkmate::assert_character(variables$rsp)
115	17x	checkmate::assert_character(variables$arm)
116	17x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
117	17x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
118	17x	assert_df_with_variables(data, variables)
119	17x	checkmate::assert_string(label_all)
120
121		# Add All Patients.
122	17x	result_all <- h_proportion_df(data[[variables$rsp]], data[[variables$arm]])
123	17x	result_all$subgroup <- label_all
124	17x	result_all$var <- "ALL"
125	17x	result_all$var_label <- label_all
126	17x	result_all$row_type <- "content"
127
128		# Add Subgroups.
129	17x	if (is.null(variables$subgroups)) {
130	3x	result_all
131		} else {
132	14x	l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
133
134	14x	l_result <- lapply(l_data, function(grp) {
135	58x	result <- h_proportion_df(grp$df[[variables$rsp]], grp$df[[variables$arm]])
136	58x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
137	58x	cbind(result, result_labels)
138		})
139	14x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
140	14x	result_subgroups$row_type <- "analysis"
141
142	14x	rbind(
143	14x	result_all,
144	14x	result_subgroups
145		)
146		}
147		}
148
149		#' @describeIn h_response_subgroups Helper to prepare a data frame with estimates of
150		#' the odds ratio between a treatment and a control arm.
151		#'
152		#' @inheritParams response_subgroups
153		#' @param strata_data (`factor`, `data.frame`, or `NULL`)\cr required if stratified analysis is performed.
154		#'
155		#' @return
156		#' * `h_odds_ratio_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`, `conf_level`, and
157		#' optionally `pval` and `pval_label`.
158		#'
159		#' @examples
160		#' # Unstratatified analysis.
161		#' h_odds_ratio_df(
162		#' c(TRUE, FALSE, FALSE, TRUE),
163		#' arm = factor(c("A", "A", "B", "B"), levels = c("A", "B"))
164		#' )
165		#'
166		#' # Include p-value.
167		#' h_odds_ratio_df(adrs_f$rsp, adrs_f$ARM, method = "chisq")
168		#'
169		#' # Stratatified analysis.
170		#' h_odds_ratio_df(
171		#' rsp = adrs_f$rsp,
172		#' arm = adrs_f$ARM,
173		#' strata_data = adrs_f[, c("STRATA1", "STRATA2")],
174		#' method = "cmh"
175		#' )
176		#'
177		#' @export
178		h_odds_ratio_df <- function(rsp, arm, strata_data = NULL, conf_level = 0.95, method = NULL) {
179	84x	assert_valid_factor(arm, n.levels = 2, len = length(rsp))
180
181	84x	df_rsp <- data.frame(
182	84x	rsp = rsp,
183	84x	arm = arm
184		)
185
186	84x	if (!is.null(strata_data)) {
187	11x	strata_var <- interaction(strata_data, drop = TRUE)
188	11x	strata_name <- "strata"
189
190	11x	assert_valid_factor(strata_var, len = nrow(df_rsp))
191
192	11x	df_rsp[[strata_name]] <- strata_var
193		} else {
194	73x	strata_name <- NULL
195		}
196
197	84x	l_df <- split(df_rsp, arm)
198
199	84x	if (nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) > 0) {
200		# Odds ratio and CI.
201	82x	result_odds_ratio <- s_odds_ratio(
202	82x	df = l_df[[2]],
203	82x	.var = "rsp",
204	82x	.ref_group = l_df[[1]],
205	82x	.in_ref_col = FALSE,
206	82x	.df_row = df_rsp,
207	82x	variables = list(arm = "arm", strata = strata_name),
208	82x	conf_level = conf_level
209		)
210
211	82x	df <- data.frame(
212		# Dummy column needed downstream to create a nested header.
213	82x	arm = " ",
214	82x	n_tot = unname(result_odds_ratio$n_tot["n_tot"]),
215	82x	or = unname(result_odds_ratio$or_ci["est"]),
216	82x	lcl = unname(result_odds_ratio$or_ci["lcl"]),
217	82x	ucl = unname(result_odds_ratio$or_ci["ucl"]),
218	82x	conf_level = conf_level,
219	82x	stringsAsFactors = FALSE
220		)
221
222	82x	if (!is.null(method)) {
223		# Test for difference.
224	44x	result_test <- s_test_proportion_diff(
225	44x	df = l_df[[2]],
226	44x	.var = "rsp",
227	44x	.ref_group = l_df[[1]],
228	44x	.in_ref_col = FALSE,
229	44x	variables = list(strata = strata_name),
230	44x	method = method
231		)
232
233	44x	df$pval <- as.numeric(result_test$pval)
234	44x	df$pval_label <- obj_label(result_test$pval)
235		}
236
237		# In those cases cannot go through the model so will obtain n_tot from data.
238		} else if (
239	2x	(nrow(l_df[[1]]) == 0 && nrow(l_df[[2]]) > 0) \|\|
240	2x	(nrow(l_df[[1]]) > 0 && nrow(l_df[[2]]) == 0)
241		) {
242	2x	df <- data.frame(
243		# Dummy column needed downstream to create a nested header.
244	2x	arm = " ",
245	2x	n_tot = sum(stats::complete.cases(df_rsp)),
246	2x	or = NA,
247	2x	lcl = NA,
248	2x	ucl = NA,
249	2x	conf_level = conf_level,
250	2x	stringsAsFactors = FALSE
251		)
252	2x	if (!is.null(method)) {
253	2x	df$pval <- NA
254	2x	df$pval_label <- NA
255		}
256		} else {
257	!	df <- data.frame(
258		# Dummy column needed downstream to create a nested header.
259	!	arm = " ",
260	!	n_tot = 0L,
261	!	or = NA,
262	!	lcl = NA,
263	!	ucl = NA,
264	!	conf_level = conf_level,
265	!	stringsAsFactors = FALSE
266		)
267
268	!	if (!is.null(method)) {
269	!	df$pval <- NA
270	!	df$pval_label <- NA
271		}
272		}
273
274	84x	df
275		}
276
277		#' @describeIn h_response_subgroups Summarizes estimates of the odds ratio between a treatment and a control
278		#' arm across subgroups in a data frame. `variables` corresponds to the names of variables found in
279		#' `data`, passed as a named list and requires elements `rsp`, `arm` and optionally `subgroups`
280		#' and `strata`. `groups_lists` optionally specifies groupings for `subgroups` variables.
281		#'
282		#' @return
283		#' * `h_odds_ratio_subgroups_df()` returns a `data.frame` with columns `arm`, `n_tot`, `or`, `lcl`, `ucl`,
284		#' `conf_level`, `subgroup`, `var`, `var_label`, and `row_type`.
285		#'
286		#' @examples
287		#' # Unstratified analysis.
288		#' h_odds_ratio_subgroups_df(
289		#' variables = list(rsp = "rsp", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
290		#' data = adrs_f
291		#' )
292		#'
293		#' # Stratified analysis.
294		#' h_odds_ratio_subgroups_df(
295		#' variables = list(
296		#' rsp = "rsp",
297		#' arm = "ARM",
298		#' subgroups = c("SEX", "BMRKR2"),
299		#' strata = c("STRATA1", "STRATA2")
300		#' ),
301		#' data = adrs_f
302		#' )
303		#'
304		#' # Define groupings of BMRKR2 levels.
305		#' h_odds_ratio_subgroups_df(
306		#' variables = list(
307		#' rsp = "rsp",
308		#' arm = "ARM",
309		#' subgroups = c("SEX", "BMRKR2")
310		#' ),
311		#' data = adrs_f,
312		#' groups_lists = list(
313		#' BMRKR2 = list(
314		#' "low" = "LOW",
315		#' "low/medium" = c("LOW", "MEDIUM"),
316		#' "low/medium/high" = c("LOW", "MEDIUM", "HIGH")
317		#' )
318		#' )
319		#' )
320		#'
321		#' @export
322		h_odds_ratio_subgroups_df <- function(variables,
323		data,
324		groups_lists = list(),
325		conf_level = 0.95,
326		method = NULL,
327		label_all = "All Patients") {
328	18x	if ("strat" %in% names(variables)) {
329	!	warning(
330	!	"Warning: the `strat` element name of the `variables` list argument to `h_odds_ratio_subgroups_df() ",
331	!	"was deprecated in tern 0.9.4.\n ",
332	!	"Please use the name `strata` instead of `strat` in the `variables` argument."
333		)
334	!	variables[["strata"]] <- variables[["strat"]]
335		}
336
337	18x	checkmate::assert_character(variables$rsp)
338	18x	checkmate::assert_character(variables$arm)
339	18x	checkmate::assert_character(variables$subgroups, null.ok = TRUE)
340	18x	checkmate::assert_character(variables$strata, null.ok = TRUE)
341	18x	assert_df_with_factors(data, list(val = variables$arm), min.levels = 2, max.levels = 2)
342	18x	assert_df_with_variables(data, variables)
343	18x	checkmate::assert_string(label_all)
344
345	18x	strata_data <- if (is.null(variables$strata)) {
346	16x	NULL
347		} else {
348	2x	data[, variables$strata, drop = FALSE]
349		}
350
351		# Add All Patients.
352	18x	result_all <- h_odds_ratio_df(
353	18x	rsp = data[[variables$rsp]],
354	18x	arm = data[[variables$arm]],
355	18x	strata_data = strata_data,
356	18x	conf_level = conf_level,
357	18x	method = method
358		)
359	18x	result_all$subgroup <- label_all
360	18x	result_all$var <- "ALL"
361	18x	result_all$var_label <- label_all
362	18x	result_all$row_type <- "content"
363
364	18x	if (is.null(variables$subgroups)) {
365	3x	result_all
366		} else {
367	15x	l_data <- h_split_by_subgroups(data, variables$subgroups, groups_lists = groups_lists)
368
369	15x	l_result <- lapply(l_data, function(grp) {
370	62x	grp_strata_data <- if (is.null(variables$strata)) {
371	54x	NULL
372		} else {
373	8x	grp$df[, variables$strata, drop = FALSE]
374		}
375
376	62x	result <- h_odds_ratio_df(
377	62x	rsp = grp$df[[variables$rsp]],
378	62x	arm = grp$df[[variables$arm]],
379	62x	strata_data = grp_strata_data,
380	62x	conf_level = conf_level,
381	62x	method = method
382		)
383	62x	result_labels <- grp$df_labels[rep(1, times = nrow(result)), ]
384	62x	cbind(result, result_labels)
385		})
386
387	15x	result_subgroups <- do.call(rbind, args = c(l_result, make.row.names = FALSE))
388	15x	result_subgroups$row_type <- "analysis"
389
390	15x	rbind(
391	15x	result_all,
392	15x	result_subgroups
393		)
394		}
395		}

1		#' Occurrence table pruning
2		#'
3		#' @description `r lifecycle::badge("stable")`
4		#'
5		#' Family of constructor and condition functions to flexibly prune occurrence tables.
6		#' The condition functions always return whether the row result is higher than the threshold.
7		#' Since they are of class [CombinationFunction()] they can be logically combined with other condition
8		#' functions.
9		#'
10		#' @note Since most table specifications are worded positively, we name our constructor and condition
11		#' functions positively, too. However, note that the result of [keep_rows()] says what
12		#' should be pruned, to conform with the [rtables::prune_table()] interface.
13		#'
14		#' @examples
15		#' \donttest{
16		#' tab <- basic_table() %>%
17		#' split_cols_by("ARM") %>%
18		#' split_rows_by("RACE") %>%
19		#' split_rows_by("STRATA1") %>%
20		#' summarize_row_groups() %>%
21		#' analyze_vars("COUNTRY", .stats = "count_fraction") %>%
22		#' build_table(DM)
23		#' }
24		#'
25		#' @name prune_occurrences
26		NULL
27
28		#' @describeIn prune_occurrences Constructor for creating pruning functions based on
29		#' a row condition function. This removes all analysis rows (`TableRow`) that should be
30		#' pruned, i.e., don't fulfill the row condition. It removes the sub-tree if there are no
31		#' children left.
32		#'
33		#' @param row_condition (`CombinationFunction`)\cr condition function which works on individual
34		#' analysis rows and flags whether these should be kept in the pruned table.
35		#'
36		#' @return
37		#' * `keep_rows()` returns a pruning function that can be used with [rtables::prune_table()]
38		#' to prune an `rtables` table.
39		#'
40		#' @examples
41		#' \donttest{
42		#' # `keep_rows`
43		#' is_non_empty <- !CombinationFunction(all_zero_or_na)
44		#' prune_table(tab, keep_rows(is_non_empty))
45		#' }
46		#'
47		#' @export
48		keep_rows <- function(row_condition) {
49	6x	checkmate::assert_function(row_condition)
50	6x	function(table_tree) {
51	2256x	if (inherits(table_tree, "TableRow")) {
52	1872x	return(!row_condition(table_tree))
53		}
54	384x	children <- tree_children(table_tree)
55	384x	identical(length(children), 0L)
56		}
57		}
58
59		#' @describeIn prune_occurrences Constructor for creating pruning functions based on
60		#' a condition for the (first) content row in leaf tables. This removes all leaf tables where
61		#' the first content row does not fulfill the condition. It does not check individual rows.
62		#' It then proceeds recursively by removing the sub tree if there are no children left.
63		#'
64		#' @param content_row_condition (`CombinationFunction`)\cr condition function which works on individual
65		#' first content rows of leaf tables and flags whether these leaf tables should be kept in the pruned table.
66		#'
67		#' @return
68		#' * `keep_content_rows()` returns a pruning function that checks the condition on the first content
69		#' row of leaf tables in the table.
70		#'
71		#' @examples
72		#' # `keep_content_rows`
73		#' \donttest{
74		#' more_than_twenty <- has_count_in_cols(atleast = 20L, col_names = names(tab))
75		#' prune_table(tab, keep_content_rows(more_than_twenty))
76		#' }
77		#'
78		#' @export
79		keep_content_rows <- function(content_row_condition) {
80	1x	checkmate::assert_function(content_row_condition)
81	1x	function(table_tree) {
82	166x	if (is_leaf_table(table_tree)) {
83	24x	content_row <- h_content_first_row(table_tree)
84	24x	return(!content_row_condition(content_row))
85		}
86	142x	if (inherits(table_tree, "DataRow")) {
87	120x	return(FALSE)
88		}
89	22x	children <- tree_children(table_tree)
90	22x	identical(length(children), 0L)
91		}
92		}
93
94		#' @describeIn prune_occurrences Constructor for creating condition functions on total counts in the specified columns.
95		#'
96		#' @param atleast (`numeric(1)`)\cr threshold which should be met in order to keep the row.
97		#' @param ... arguments for row or column access, see [`rtables_access`]: either `col_names` (`character`) including
98		#' the names of the columns which should be used, or alternatively `col_indices` (`integer`) giving the indices
99		#' directly instead.
100		#'
101		#' @return
102		#' * `has_count_in_cols()` returns a condition function that sums the counts in the specified column.
103		#'
104		#' @examples
105		#' \donttest{
106		#' more_than_one <- has_count_in_cols(atleast = 1L, col_names = names(tab))
107		#' prune_table(tab, keep_rows(more_than_one))
108		#' }
109		#'
110		#' @export
111		has_count_in_cols <- function(atleast, ...) {
112	6x	checkmate::assert_count(atleast)
113	6x	CombinationFunction(function(table_row) {
114	337x	row_counts <- h_row_counts(table_row, ...)
115	337x	total_count <- sum(row_counts)
116	337x	total_count >= atleast
117		})
118		}
119
120		#' @describeIn prune_occurrences Constructor for creating condition functions on any of the counts in
121		#' the specified columns satisfying a threshold.
122		#'
123		#' @param atleast (`numeric(1)`)\cr threshold which should be met in order to keep the row.
124		#'
125		#' @return
126		#' * `has_count_in_any_col()` returns a condition function that compares the counts in the
127		#' specified columns with the threshold.
128		#'
129		#' @examples
130		#' \donttest{
131		#' # `has_count_in_any_col`
132		#' any_more_than_one <- has_count_in_any_col(atleast = 1L, col_names = names(tab))
133		#' prune_table(tab, keep_rows(any_more_than_one))
134		#' }
135		#'
136		#' @export
137		has_count_in_any_col <- function(atleast, ...) {
138	3x	checkmate::assert_count(atleast)
139	3x	CombinationFunction(function(table_row) {
140	3x	row_counts <- h_row_counts(table_row, ...)
141	3x	any(row_counts >= atleast)
142		})
143		}
144
145		#' @describeIn prune_occurrences Constructor for creating condition functions on total fraction in
146		#' the specified columns.
147		#'
148		#' @return
149		#' * `has_fraction_in_cols()` returns a condition function that sums the counts in the
150		#' specified column, and computes the fraction by dividing by the total column counts.
151		#'
152		#' @examples
153		#' \donttest{
154		#' # `has_fraction_in_cols`
155		#' more_than_five_percent <- has_fraction_in_cols(atleast = 0.05, col_names = names(tab))
156		#' prune_table(tab, keep_rows(more_than_five_percent))
157		#' }
158		#'
159		#' @export
160		has_fraction_in_cols <- function(atleast, ...) {
161	4x	assert_proportion_value(atleast, include_boundaries = TRUE)
162	4x	CombinationFunction(function(table_row) {
163	306x	row_counts <- h_row_counts(table_row, ...)
164	306x	total_count <- sum(row_counts)
165	306x	col_counts <- h_col_counts(table_row, ...)
166	306x	total_n <- sum(col_counts)
167	306x	total_percent <- total_count / total_n
168	306x	total_percent >= atleast
169		})
170		}
171
172		#' @describeIn prune_occurrences Constructor for creating condition functions on any fraction in
173		#' the specified columns.
174		#'
175		#' @return
176		#' * `has_fraction_in_any_col()` returns a condition function that looks at the fractions
177		#' in the specified columns and checks whether any of them fulfill the threshold.
178		#'
179		#' @examples
180		#' \donttest{
181		#' # `has_fraction_in_any_col`
182		#' any_atleast_five_percent <- has_fraction_in_any_col(atleast = 0.05, col_names = names(tab))
183		#' prune_table(tab, keep_rows(any_atleast_five_percent))
184		#' }
185		#'
186		#' @export
187		has_fraction_in_any_col <- function(atleast, ...) {
188	3x	assert_proportion_value(atleast, include_boundaries = TRUE)
189	3x	CombinationFunction(function(table_row) {
190	3x	row_fractions <- h_row_fractions(table_row, ...)
191	3x	any(row_fractions >= atleast)
192		})
193		}
194
195		#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
196		#' between the fractions reported in each specified column.
197		#'
198		#' @return
199		#' * `has_fractions_difference()` returns a condition function that extracts the fractions of each
200		#' specified column, and computes the difference of the minimum and maximum.
201		#'
202		#' @examples
203		#' \donttest{
204		#' # `has_fractions_difference`
205		#' more_than_five_percent_diff <- has_fractions_difference(atleast = 0.05, col_names = names(tab))
206		#' prune_table(tab, keep_rows(more_than_five_percent_diff))
207		#' }
208		#'
209		#' @export
210		has_fractions_difference <- function(atleast, ...) {
211	4x	assert_proportion_value(atleast, include_boundaries = TRUE)
212	4x	CombinationFunction(function(table_row) {
213	246x	fractions <- h_row_fractions(table_row, ...)
214	246x	difference <- diff(range(fractions))
215	246x	difference >= atleast
216		})
217		}
218
219		#' @describeIn prune_occurrences Constructor for creating condition function that checks the difference
220		#' between the counts reported in each specified column.
221		#'
222		#' @return
223		#' * `has_counts_difference()` returns a condition function that extracts the counts of each
224		#' specified column, and computes the difference of the minimum and maximum.
225		#'
226		#' @examples
227		#' \donttest{
228		#' more_than_one_diff <- has_counts_difference(atleast = 1L, col_names = names(tab))
229		#' prune_table(tab, keep_rows(more_than_one_diff))
230		#' }
231		#'
232		#' @export
233		has_counts_difference <- function(atleast, ...) {
234	4x	checkmate::assert_count(atleast)
235	4x	CombinationFunction(function(table_row) {
236	30x	counts <- h_row_counts(table_row, ...)
237	30x	difference <- diff(range(counts))
238	30x	difference >= atleast
239		})
240		}

1		#' Re-implemented `range()` default S3 method for numerical objects
2		#'
3		#' This function returns `c(NA, NA)` instead of `c(-Inf, Inf)` for zero-length data
4		#' without any warnings.
5		#'
6		#' @param x (`numeric`)\cr a sequence of numbers for which the range is computed.
7		#' @param na.rm (`flag`)\cr flag indicating if `NA` should be omitted.
8		#' @param finite (`flag`)\cr flag indicating if non-finite elements should be removed.
9		#'
10		#' @return A 2-element vector of class `numeric`.
11		#'
12		#' @keywords internal
13		range_noinf <- function(x, na.rm = FALSE, finite = FALSE) { # nolint
14
15	1884x	checkmate::assert_numeric(x)
16
17	1884x	if (finite) {
18	24x	x <- x[is.finite(x)] # removes NAs too
19	1860x	} else if (na.rm) {
20	708x	x <- x[!is.na(x)]
21		}
22
23	1884x	if (length(x) == 0) {
24	111x	rval <- c(NA, NA)
25	111x	mode(rval) <- typeof(x)
26		} else {
27	1773x	rval <- c(min(x, na.rm = FALSE), max(x, na.rm = FALSE))
28		}
29
30	1884x	return(rval)
31		}
32
33		#' Utility function to create label for confidence interval
34		#'
35		#' @description `r lifecycle::badge("stable")`
36		#'
37		#' @inheritParams argument_convention
38		#'
39		#' @return A `string`.
40		#'
41		#' @export
42		f_conf_level <- function(conf_level) {
43	8277x	assert_proportion_value(conf_level)
44	8275x	paste0(conf_level * 100, "% CI")
45		}
46
47		#' Utility function to create label for p-value
48		#'
49		#' @description `r lifecycle::badge("stable")`
50		#'
51		#' @param test_mean (`numeric(1)`)\cr mean value to test under the null hypothesis.
52		#'
53		#' @return A `string`.
54		#'
55		#' @export
56		f_pval <- function(test_mean) {
57	1145x	checkmate::assert_numeric(test_mean, len = 1)
58	1143x	paste0("p-value (H0: mean = ", test_mean, ")")
59		}
60
61		#' Utility function to return a named list of covariate names
62		#'
63		#' @param covariates (`character`)\cr a vector that can contain single variable names (such as
64		#' `"X1"`), and/or interaction terms indicated by `"X1 * X2"`.
65		#'
66		#' @return A named `list` of `character` vector.
67		#'
68		#' @keywords internal
69		get_covariates <- function(covariates) {
70	14x	checkmate::assert_character(covariates)
71	12x	cov_vars <- unique(trimws(unlist(strsplit(covariates, "\\*"))))
72	12x	stats::setNames(as.list(cov_vars), cov_vars)
73		}
74
75		#' Replicate entries of a vector if required
76		#'
77		#' @description `r lifecycle::badge("stable")`
78		#'
79		#' Replicate entries of a vector if required.
80		#'
81		#' @inheritParams argument_convention
82		#' @param n (`integer(1)`)\cr number of entries that are needed.
83		#'
84		#' @return `x` if it has the required length already or is `NULL`,
85		#' otherwise if it is scalar the replicated version of it with `n` entries.
86		#'
87		#' @note This function will fail if `x` is not of length `n` and/or is not a scalar.
88		#'
89		#' @export
90		to_n <- function(x, n) {
91	5x	if (is.null(x)) {
92	1x	NULL
93	4x	} else if (length(x) == 1) {
94	1x	rep(x, n)
95	3x	} else if (length(x) == n) {
96	2x	x
97		} else {
98	1x	stop("dimension mismatch")
99		}
100		}
101
102		#' Check element dimension
103		#'
104		#' Checks if the elements in `...` have the same dimension.
105		#'
106		#' @param ... (`data.frame` or `vector`)\cr any data frames or vectors.
107		#' @param omit_null (`flag`)\cr whether `NULL` elements in `...` should be omitted from the check.
108		#'
109		#' @return A `logical` value.
110		#'
111		#' @keywords internal
112		check_same_n <- function(..., omit_null = TRUE) {
113	2x	dots <- list(...)
114
115	2x	n_list <- Map(
116	2x	function(x, name) {
117	5x	if (is.null(x)) {
118	!	if (omit_null) {
119	2x	NA_integer_
120		} else {
121	!	stop("arg", name, "is not supposed to be NULL")
122		}
123	5x	} else if (is.data.frame(x)) {
124	!	nrow(x)
125	5x	} else if (is.atomic(x)) {
126	5x	length(x)
127		} else {
128	!	stop("data structure for ", name, "is currently not supported")
129		}
130		},
131	2x	dots, names(dots)
132		)
133
134	2x	n <- stats::na.omit(unlist(n_list))
135
136	2x	if (length(unique(n)) > 1) {
137	!	sel <- which(n != n[1])
138	!	stop("Dimension mismatch:", paste(names(n)[sel], collapse = ", "), " do not have N=", n[1])
139		}
140
141	2x	TRUE
142		}
143
144		#' Utility function to check if a float value is equal to another float value
145		#'
146		#' Uses `.Machine$double.eps` as the tolerance for the comparison.
147		#'
148		#' @param x (`numeric(1)`)\cr a float number.
149		#' @param y (`numeric(1)`)\cr a float number.
150		#'
151		#' @return `TRUE` if identical, otherwise `FALSE`.
152		#'
153		#' @keywords internal
154		.is_equal_float <- function(x, y) {
155	2990x	checkmate::assert_number(x)
156	2990x	checkmate::assert_number(y)
157
158		# Define a tolerance
159	2990x	tolerance <- .Machine$double.eps
160
161		# Check if x is close enough to y
162	2990x	abs(x - y) < tolerance
163		}
164
165		#' Make names without dots
166		#'
167		#' @param nams (`character`)\cr vector of original names.
168		#'
169		#' @return A `character` `vector` of proper names, which does not use dots in contrast to [make.names()].
170		#'
171		#' @keywords internal
172		make_names <- function(nams) {
173	6x	orig <- make.names(nams)
174	6x	gsub(".", "", x = orig, fixed = TRUE)
175		}
176
177		#' Conversion of months to days
178		#'
179		#' @description `r lifecycle::badge("stable")`
180		#'
181		#' Conversion of months to days. This is an approximative calculation because it
182		#' considers each month as having an average of 30.4375 days.
183		#'
184		#' @param x (`numeric(1)`)\cr time in months.
185		#'
186		#' @return A `numeric` vector with the time in days.
187		#'
188		#' @examples
189		#' x <- c(13.25, 8.15, 1, 2.834)
190		#' month2day(x)
191		#'
192		#' @export
193		month2day <- function(x) {
194	1x	checkmate::assert_numeric(x)
195	1x	x * 30.4375
196		}
197
198		#' Conversion of days to months
199		#'
200		#' @param x (`numeric(1)`)\cr time in days.
201		#'
202		#' @return A `numeric` vector with the time in months.
203		#'
204		#' @examples
205		#' x <- c(403, 248, 30, 86)
206		#' day2month(x)
207		#'
208		#' @export
209		day2month <- function(x) {
210	19x	checkmate::assert_numeric(x)
211	19x	x / 30.4375
212		}
213
214		#' Return an empty numeric if all elements are `NA`.
215		#'
216		#' @param x (`numeric`)\cr vector.
217		#'
218		#' @return An empty `numeric` if all elements of `x` are `NA`, otherwise `x`.
219		#'
220		#' @examples
221		#' x <- c(NA, NA, NA)
222		#' # Internal function - empty_vector_if_na
223		#' @keywords internal
224		empty_vector_if_na <- function(x) {
225	1017x	if (all(is.na(x))) {
226	310x	numeric()
227		} else {
228	707x	x
229		}
230		}
231
232		#' Element-wise combination of two vectors
233		#'
234		#' @param x (`vector`)\cr first vector to combine.
235		#' @param y (`vector`)\cr second vector to combine.
236		#'
237		#' @return A `list` where each element combines corresponding elements of `x` and `y`.
238		#'
239		#' @examples
240		#' combine_vectors(1:3, 4:6)
241		#'
242		#' @export
243		combine_vectors <- function(x, y) {
244	51x	checkmate::assert_vector(x)
245	51x	checkmate::assert_vector(y, len = length(x))
246
247	51x	result <- lapply(as.data.frame(rbind(x, y)), `c`)
248	51x	names(result) <- NULL
249	51x	result
250		}
251
252		#' Extract elements by name
253		#'
254		#' This utility function extracts elements from a vector `x` by `names`.
255		#' Differences to the standard `[` function are:
256		#'
257		#' - If `x` is `NULL`, then still always `NULL` is returned (same as in base function).
258		#' - If `x` is not `NULL`, then the intersection of its names is made with `names` and those
259		#' elements are returned. That is, `names` which don't appear in `x` are not returned as `NA`s.
260		#'
261		#' @param x (named `vector`)\cr where to extract named elements from.
262		#' @param names (`character`)\cr vector of names to extract.
263		#'
264		#' @return `NULL` if `x` is `NULL`, otherwise the extracted elements from `x`.
265		#'
266		#' @keywords internal
267		extract_by_name <- function(x, names) {
268	3x	if (is.null(x)) {
269	1x	return(NULL)
270		}
271	2x	checkmate::assert_named(x)
272	2x	checkmate::assert_character(names)
273	2x	which_extract <- intersect(names(x), names)
274	2x	if (length(which_extract) > 0) {
275	1x	x[which_extract]
276		} else {
277	1x	NULL
278		}
279		}
280
281		#' Labels for adverse event baskets
282		#'
283		#' @description `r lifecycle::badge("stable")`
284		#'
285		#' @param aesi (`character`)\cr vector with standardized MedDRA query name (e.g. `SMQxxNAM`) or customized query
286		#' name (e.g. `CQxxNAM`).
287		#' @param scope (`character`)\cr vector with scope of query (e.g. `SMQxxSC`).
288		#'
289		#' @return A `string` with the standard label for the AE basket.
290		#'
291		#' @examples
292		#' adae <- tern_ex_adae
293		#'
294		#' # Standardized query label includes scope.
295		#' aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC)
296		#'
297		#' # Customized query label.
298		#' aesi_label(adae$CQ01NAM)
299		#'
300		#' @export
301		aesi_label <- function(aesi, scope = NULL) {
302	4x	checkmate::assert_character(aesi)
303	4x	checkmate::assert_character(scope, null.ok = TRUE)
304	4x	aesi_label <- obj_label(aesi)
305	4x	aesi <- sas_na(aesi)
306	4x	aesi <- unique(aesi)[!is.na(unique(aesi))]
307
308	4x	lbl <- if (length(aesi) == 1 && !is.null(scope)) {
309	1x	scope <- sas_na(scope)
310	1x	scope <- unique(scope)[!is.na(unique(scope))]
311	1x	checkmate::assert_string(scope)
312	1x	paste0(aesi, " (", scope, ")")
313	4x	} else if (length(aesi) == 1 && is.null(scope)) {
314	1x	aesi
315		} else {
316	2x	aesi_label
317		}
318
319	4x	lbl
320		}
321
322		#' Indicate study arm variable in formula
323		#'
324		#' We use `study_arm` to indicate the study arm variable in `tern` formulas.
325		#'
326		#' @param x arm information
327		#'
328		#' @return `x`
329		#'
330		#' @keywords internal
331		study_arm <- function(x) {
332	!	structure(x, varname = deparse(substitute(x)))
333		}
334
335		#' Smooth function with optional grouping
336		#'
337		#' @description `r lifecycle::badge("stable")`
338		#'
339		#' This produces `loess` smoothed estimates of `y` with Student confidence intervals.
340		#'
341		#' @param df (`data.frame`)\cr data set containing all analysis variables.
342		#' @param x (`string`)\cr x column name.
343		#' @param y (`string`)\cr y column name.
344		#' @param groups (`character` or `NULL`)\cr vector with optional grouping variables names.
345		#' @param level (`proportion`)\cr level of confidence interval to use (0.95 by default).
346		#'
347		#' @return A `data.frame` with original `x`, smoothed `y`, `ylow`, and `yhigh`, and
348		#' optional `groups` variables formatted as `factor` type.
349		#'
350		#' @export
351		get_smooths <- function(df, x, y, groups = NULL, level = 0.95) {
352	5x	checkmate::assert_data_frame(df)
353	5x	df_cols <- colnames(df)
354	5x	checkmate::assert_string(x)
355	5x	checkmate::assert_subset(x, df_cols)
356	5x	checkmate::assert_numeric(df[[x]])
357	5x	checkmate::assert_string(y)
358	5x	checkmate::assert_subset(y, df_cols)
359	5x	checkmate::assert_numeric(df[[y]])
360
361	5x	if (!is.null(groups)) {
362	4x	checkmate::assert_character(groups)
363	4x	checkmate::assert_subset(groups, df_cols)
364		}
365
366	5x	smooths <- function(x, y) {
367	18x	stats::predict(stats::loess(y ~ x), se = TRUE)
368		}
369
370	5x	if (!is.null(groups)) {
371	4x	cc <- stats::complete.cases(df[c(x, y, groups)])
372	4x	df_c <- df[cc, c(x, y, groups)]
373	4x	df_c_ordered <- df_c[do.call("order", as.list(df_c[, groups, drop = FALSE])), , drop = FALSE]
374	4x	df_c_g <- data.frame(Map(as.factor, df_c_ordered[groups]))
375
376	4x	df_smooth_raw <-
377	4x	by(df_c_ordered, df_c_g, function(d) {
378	17x	plx <- smooths(d[[x]], d[[y]])
379	17x	data.frame(
380	17x	x = d[[x]],
381	17x	y = plx$fit,
382	17x	ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
383	17x	yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
384		)
385		})
386
387	4x	df_smooth <- do.call(rbind, df_smooth_raw)
388	4x	df_smooth[groups] <- df_c_g
389
390	4x	df_smooth
391		} else {
392	1x	cc <- stats::complete.cases(df[c(x, y)])
393	1x	df_c <- df[cc, ]
394	1x	plx <- smooths(df_c[[x]], df_c[[y]])
395
396	1x	df_smooth <- data.frame(
397	1x	x = df_c[[x]],
398	1x	y = plx$fit,
399	1x	ylow = plx$fit - stats::qt(level, plx$df) * plx$se.fit,
400	1x	yhigh = plx$fit + stats::qt(level, plx$df) * plx$se.fit
401		)
402
403	1x	df_smooth
404		}
405		}
406
407		#' Number of available (non-missing entries) in a vector
408		#'
409		#' Small utility function for better readability.
410		#'
411		#' @param x (`vector`)\cr vector in which to count non-missing values.
412		#'
413		#' @return Number of non-missing values.
414		#'
415		#' @keywords internal
416		n_available <- function(x) {
417	355x	sum(!is.na(x))
418		}
419
420		#' Reapply variable labels
421		#'
422		#' This is a helper function that is used in tests.
423		#'
424		#' @param x (`vector`)\cr vector of elements that needs new labels.
425		#' @param varlabels (`character`)\cr vector of labels for `x`.
426		#' @param ... further parameters to be added to the list.
427		#'
428		#' @return `x` with variable labels reapplied.
429		#'
430		#' @export
431		reapply_varlabels <- function(x, varlabels, ...) {
432	11x	named_labels <- c(as.list(varlabels), list(...))
433	11x	formatters::var_labels(x)[names(named_labels)] <- as.character(named_labels)
434	11x	x
435		}
436
437		# Wrapper function of survival::clogit so that when model fitting failed, a more useful message would show
438		clogit_with_tryCatch <- function(formula, data, ...) { # nolint
439	33x	tryCatch(
440	33x	survival::clogit(formula = formula, data = data, ...),
441	33x	error = function(e) stop("model not built successfully with survival::clogit")
442		)
443		}